US 2020/0368491 A1 Poltorak ( 43 ) Pub

US 20200368491A1 IN ( 19 ) United States ( 12 ) Patent Application Publication ( 10 ) Pub . No .: US 2020/0368491 A1 Poltorak ( 43 ) Pub . Date : Nov. 26 , 2020

( 54 ) DEVICE , METHOD , AND APP FOR Publication Classification FACILITATING SLEEP ( 51 ) Int. CI . A61M 21/02 ( 2006.01 ) ( 71 ) Applicant: Neuroenhancement Lab , LLC , ( 52 ) U.S. CI . Suffern , NY ( US ) CPC A61M 21/02 ( 2013.01 ) ; A61M 2021/0044 ( 72 ) Inventor: Alexander Poltorak , Monsey, NY (US ) ( 2013.01 ) ( 57 ) ABSTRACT A device , system , and method for facilitating a sleep cycle ( 21 ) Appl. No .: 16 / 883,541 in a subject, comprising selecting a waveform from a plurality of waveforms derived from brainwaves of at least ( 22 ) Filed : May 26 , 2020 one sleeping donor, wherein said waveform corresponds to at least one specific stage of sleep ; and stimulating the subject with at least one stimulus, wherein said at least one Related U.S. Application Data stimulus is at least one of an auditory stimulus and a visual ( 60 ) Provisional application No. 62 / 862,656 , filed on Jun . stimulus modulated with the selected waveform to entrain 17 , 2019 , provisional application No. 62 / 852,877 , the brain of the subject with the selected waveform to filed on May 24 , 2019 . facilitate sleep in the subject.

Identify a mental state of a first subject

The mental state of the first subject is a sleep

Yes

Capture brain activity patterns reflecting the sleep state from the first subject

Store the brain activity patterns of the sleep state in a nonvolatile memory

Retrieve the brain activity patterns of the sleep state from the non - volatile memory

Induce sleep sate in the second subject by replicating the brain activity patterns of the first subject in the second subject

Verify that the second subject is asleep Patent Application Publication Nov. 26 , 2020 Sheet 1 of 38 US 2020/0368491 A1

Identify a mental state Identify a mental state of a first subject of a first subject

No The mental state of No The mental state of the first subject is a the first subject is a sleep awake

Yes Yes Capture brain activity Capture brain activity patterns reflecting the patterns reflecting the sleep waking state of the first state from the first subject subject 15 Store the brain activity Store the brain activity patterns of the sleep state patterns of the waking state in a non - volatile memory in a non - volatile memory 35 Retrieve the brain activity Retrieve the brain activity patterns of the sleep state patterns of the waking state from the non - volatile from the non - volatile memory memory

Induce sleep sate Preventing a sleep sate in the second subject by in the second subject by replicating the brain activity replicating the brain activity patterns of the first subject patterns of the first subject in the second subject in the second subject

Verify that the second Verify that the second subject is asleep subject is awake Fig . 1 Fig . 2 Patent Application Publication Nov. 26 , 2020 Sheet 2 of 38 US 2020/0368491 A1

Identify a sleep stage of a first subject Record an EEG / MEG of of a sleeping healthy donor

NO The sleep stage of the first subject is a Process the recorded EEG / desired sleep stage MEG to remove noise

Yes Save the processed EEG / MEG of the donor Record brainwaves of the first subject in a non - volatile memory

Identify at least one Retrieve the processed EEG / dominant frequency in the MEG of the donor from the recorded brainwaves nonvolatile memory

Modulate sad at least one dominant frequency on at " Playback " the processed least one stimulus EEG / MEG to a recipient via transcranial electrical magnetic stimulation to improve quality of sleep in Replicate the sleeping stage the recipient of the first subject in the second subject by stimulating the second subject with said at least one stimulus ] Verify that the sleep stage of the second subject is the desired sleep stage Fig . 3 Fig . 4 Patent Application Publication Nov. 26 , 2020 Sheet 3 of 38 US 2020/0368491 A1

Record a multi - channel EEG / MEG of of a plurality of healthy Record a multi - channel EEG / sleeping donors MEG of of a plurality of healthy sleeping donors Process the recorded multi channel EEG / MEG recordings to remove noise Process the recorded multi channel EEG / MEG recordings to remove noise Train a neural network on the recorded EEG /MEG recordings of the plurality of Perform PCA on the EEG / healthy donors to identify MEG recordings to identify characteristic frequencies characteristic frequencies associated with sleep stages associated with sleep stages

Create a database of sleep Create a database of sleep stage and their characteristic stage and their characteristic frequencies frequencies

Fig . 5 Fig . 6 Patent Application Publication Nov. 26 , 2020 Sheet 4 of 38 US 2020/0368491 A1

Record an EEG / MEG of Record a set of brain activity cycles of a first subject being in a from a first subject desirable phase of the circadian rhythm 2 ? tag the brain activity cycles with context which Process the recorded EEG / precedes or accompanies the brain activity cycles MEG to remove noise and / or compress the data 270 process the recorded set of brain activity cycles to normalize at one amplitude, frequency or time delay of Save the processed EEG / a brainwave pattern represented in the set of brain MEG of the first subject activity cycles in a non -volatile memory

preserve at least one modulation pattem imposed on a synchronized brainwave pattem representing Retrieve the processed EEG / coordination between ensembles of neurons MEG of the first subject from the non - volatile memory select a record of the processed recorded set of brain activity cycles Decompress the EEG / MEG of the first subject retrieved from the non generate a stimulus for a second subject, comprising at volatile memory least the at least one modulation pattern imposed on the synchronized bramwave pattern representing coordination between ensembles of neurons " Play back " the processed EEG /MEG to a second subject via transcranial adaptively feedback cotrolling the stimulus for the electrical /magnetic second subject based on at least a brainwave state of stimulation in order to the second subject, said generating being controlled to induce the desirable phase synchronize the brainwave state of the second subject of the circadian rhythm in with respect to the synchronized brainwave pattern of the second subject the first subject reflected in the selected record Fig . 7 Fig. 8 Patent Application Publication Nov. 26 , 2020 Sheet 5 of 38 US 2020/0368491 A1

Identify a sleep stage Identify a desired mental of a first subject state

Identify a mental state of a subject

NO The sleep stage of the first subject is desired stage Identify a phase of a dominant brainwave characteristic of the mental state of the subject Yes

Capture brain activity Apply a stimulus to the patterns reflecting the sleep subject to change the mental stage from the first subject state of the subject to the desired mental state , while synchronizing the phase of the stimulus with the phase Store the brain activity of the brainwaves of the patterns of the sleep stage subject in a nonvolatile memory

Verify that the second Retrieve the brain activity subject is in the desired patterns of the sleep stage asleep stage from the non - volatile memory

Induce sleep stage in the second subject by replicating the brain activity Fig . 10 patterns of the first subject in the second subject

Verify that the second subject is in the desired asleep stage Fig . 9 Patent Application Publication Nov. 26 , 2020 Sheet 6 of 38 US 2020/0368491 A1

Open the app on a smartphone,

sleeping donor, the waveform corresponding to a specific stage

Choose sound delivery through *****

Play the chosen sound Play the chosen sound

*** organic wavefom on the

Fig . 11 Patent Application Publication Nov. 26 , 2020 Sheet 7 of 38 US 2020/0368491 A1

app on a smartphone , tablet Open the app on a device 1310 or another mobile or wearable

Choose light saitings (69 .: 1220 settings ( e.g. , color, intensity , sound , volume ) Choose an organic waveform a sleeping donor, the waveform 1230 corresponding to a specific Choose an organic stage of sleep or a completa sleep cycle brainwaves of a sleeping Choose light delivery through donor , the waveform ambient light source of corresponding to a specific wireless LEDs positioned on stage of sleep or a complete sleep cycle Tuin on sleep stimulation by projecting the light modulated 1250 waveform through ambient light Choose light and sound or LEDs positioned near the delivery through the device or wirelessly Fig . 12 Turn on sleep stimulation using synchronized light the chosen organic

Fig . 13 Patent Application Publication Nov. 26 , 2020 Sheet 8 of 38 US 2020/0368491 A1

1510 Open the app on a mobile or personal 3000027 Download from the renule servas a Choose light and /or sound 1420 last waveform used based on the 1520 Settings (eg , color, intensity, sleep biometric data recoved from sound , volume ) the subject during previous Choose a waveform derived from the brainwaves of a 1540 sleeping donor, the waveform 1430 Choose light andior sound delivery corresponding to a specific through the device or wirelessly stage of sleep or a complete sleep cycle Turn on seco stimulation using synchronized light andior sound Choose light andior sound 1440 modulated with the chosen organic delivery through the device or Wirelessly Turn on sleep stimulation data from the subject and transinit using synchronized light and / or sound module. th analysis the chosen organic wavefom . Record Gand or other from ihe subject to measure the 1570 biometric data from the subject effecóveness of the stimulation*** and adjust the wavefor accordingly to to the device or cloud of improve the effec of simulation . analysis Adjust stimulation of the subject based on the data received from the subject Fig . 14 Fig . 15 Patent Application Publication Nov. 26 , 2020 Sheet 9 of 38 US 2020/0368491 A1

GPS H CONTROLLER

USER

Fig . 16

You 400 Hours of Sleep Fig . 17 Patent Application Publication Nov. 26 , 2020 Sheet 10 of 38 US 2020/0368491 A1

Hypnogram one sleep cycle

Stageofsleep N2 Lan N3

Fig . 18

Fig . 19 Patent Application Publication Nov. 26 , 2020 Sheet 11 of 38 US 2020/0368491 A1

Fig . 20A

we Fig . 20B

Fig . 20C wimpledMatrix.1000][ 20 5000 5000

Fig . 21A Patent Application Publication Nov. 26 , 2020 Sheet 12 of 38 US 2020/0368491 A1 snoothedmatrix(,1000) MANDA A MAN Weather in an internation

Fig . 21B smoothedMatrix,1010] Marami ay wala wasap awal perday thermalen : ?????4 - ???Fig?? . 22 - ?????? ». Fig . 23A ?????? ???????? maperwww Wine and presenza | | |/ www?????????????Fig . 23B » Fig . 24A Patent Application Publication Nov. 26 , 2020 Sheet 13 of 38 US 2020/0368491 A1 smoothediatrix[;1030] 100

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Fig . 24B (Oo8LJxLDEWpodwes Maswali

Fig . 25A smoothedMatrix[,1300) OF Wymiany bilete de mari Marrowlan

Fig . 25B sampledMatrixl,1320]

Fig . 26A smoothiediatrix[;1320] de happement OB

Fig . 26B Patent Application Publication Nov. 26 , 2020 Sheet 14 of 38 US 2020/0368491 A1 loeg.*]xLDEVIDAQUE Why mamamanythin elemeler

Fig . 27A ynoothedMatrix.1330) Myrnumpa way MAN pump perty

Fig . 27B Replicating Mental States EEG

TES Donor Recipient WWW WWWW Fig . 28 Patent Application Publication Nov. 26 , 2020 Sheet 15 of 38 US 2020/0368491 A1

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DEVICE , METHOD , AND APP FOR Myelination of axons can increase the speed of transmission FACILITATING SLEEP by segmenting the membrane depolarization process . [ 0006 ] Many of these changes over time are repetitive or CROSS REFERENCE TO RELATED rhythmic and are described as some frequency or oscillation . APPLICATIONS The field of chronobiology, for example , examines such periodic ( cyclic ) phenomena in living organisms and their [ 0001 ] The present application is a non -provisional of, and adaptation , for example , to solar and lunar - related rhythms claims benefit of priority under 35 U.S.C. § 119 ( e ) from U.S. [DeCoursey et al . ( 2003 ) . ] These cycles are also known as Provisional Patent Application No. 62 / 862,656 , filed Jun . biological rhythms. The related terms chronomics and chro 17 , 2019 , and from U.S. Provisional Patent Application No. nome have been used in some cases to describe either the 62 / 852,877 , filed May 24 , 2019 , each of which is expressly molecular mechanisms involved in chronobiological phe incorporated herein by reference in its entirety. nomena or the more quantitative aspects of chronobiology, particularly where comparison of cycles between organisms FIELD OF THE INVENTION is required . Chronobiological studies include , but are not [ 0002 ] The present invention generally relates to the field limited to , comparative anatomy, physiology, genetics , of neuromodulation and neuro enhancement and more spe molecular biology, and behavior of organisms within bio cifically to systems , methods and applications for improving logical rhythms mechanics [ DeCoursey et al . ( 2003 ) . ] . Other achievement and / or maintenance of sleep . aspects include epigenetics, development, reproduction , ecology, and evolution . BACKGROUND OF THE INVENTION [ 0007 ] The most important rhythms in chronobiology are [ 0003 ] Each reference and document cited herein is the circadian rhythms, roughly 24 - hour cycles shown by expressly incorporated herein by reference in its entirety, for physiological processes in all these organisms. It is regulated all purposes. by circadian docks. The circadian rhythms can be further [ 0004 ] Brain Computer Interface ( BCI ) : sometimes called broken down into routine cycles during the 24 -hour day a neural - control interface ( NCI ) , mind -machine interface [ Nelson R J. 2005. An Introduction to Behavioral Endocri ( MMI ) , direct neural interface ( DNI ) , or brain -machine nology . Sinauer Associates, Inc .: Massachusetts . Pg . 587. ] interface ( BMI ) , is a communication pathway between a All animals can be classified according to their activity brain and an external computerized device . BCI may allows cycles : Diurnal, which describes organisms active during for bidirectional information flow . BCIs are often directed at daytime; Nocturnal, which describes organisms active in the researching, mapping, assisting , augmenting, or repairing night and Crepuscular, which describes animals primarily human cognitive or sensory -motor functions. See , en.wiki active during the dawn and dusk hours ( ex : white - tailed deer, pedia.org/wiki/Brain-computer_interface. A bidirectional some bats ) . adaptive BCI controlling computer buyer by an anticipatory [ 0008 ] While circadian rhythms are defined as regulated brain potential, the Contingent Negative Variation ( CNV ) by endogenous processes , other biological cycles may be potential has been reported . The experiment described how regulated by exogenous signals. In some cases , multi- trophic an expectation state of the brain , manifested by CNV, systems may exhibit rhythms driven by the circadian dock of controls in a feedback loop the S2 buyer in the S1 - S2 - CNV one of the members ( which may also be influenced or reset paradigm . The obtained cognitive wave representing the by external factors ). expectation learning in the brain is named Electroexpecto [ 0009 ] Many other important cycles are also studied , gram ( EXG ). Electroencephalography ( EEG ) is the most including Infradian rhythms, which are cycles longer than a studied non - invasive interface , mainly due to its fine tem day. Examples include circannual or annual cycles that poral resolution , ease of use , portability and low set - up cost . govern migration or reproduction cycles in many plants and See Reference List Table 1 . animals, or the human menstrual cycle ; Ultradian rhythms, [ 0005 ] Time in a biological manner. Almost everything in which are cycles shorter than 24 hours, such as the 90 -min biology is subject to change overtime. These changes occur ute REM cycle , the 4 - hour nasal cycle , or the 3 - hour cycle on many different time scales , which vary greatly. For of growth hormone production ; Tidal rhythms, commonly example, there are evolutionary changes that affect entire observed in marine life, which follow the roughly 12.4 - hour populations over time rather than a single organism . Evo transition from high to low tide and back ; Lunar rhythms, lutionary changes are often slower than a human time scale which follow the lunar month ( 29.5 days ) . They are relevant, that spans many years ( usually a human lifetime ). Faster for example , to marine life , as the level of the tides is variations of the timing and duration of biological activity in modulated across the lunar cycle ; and Gene oscillations living organisms occur, for example , in many essential some genes are expressed more during certain hours of the biological processes in everyday life : in humans and ani day than during other hours. mals , these variations occur , for example , in eating, sleep [ 0010 ] Within each cycle , the time period during which ing , mating , hibernating, migration , cellular regeneration , the process is more active is called the acrophase [Refinetti , etc. Other fast changes may include the transmission of a Roberto ( 2006 ) . Circadian Physiology . CRC Press / Taylor & neural signal, for example, through a synapse such as the Francis Group . ISBN 0-8493-2233-2 . Lay summary ]. When calyx of held , a particularly large synapse in the auditory the process is less active , the cycle is in its bathyphase or central nervous system of mammals that can reach trans trough phase . The particular moment of highest activity is mission frequencies of up to 50 Hz . nth recruitment modu the peak or maximum ; the lowest point is the nadir . How lation , the effective frequencies can be higher. A single nerve high ( or low ) the process gets is measured by the amplitude . impulse can reach a speed as high as one hundred meters [ 0011 ] The sleep cycle and the ultradian rhythms: The ( 0.06 mile ) per second ( Kraus, David . Concepts in Modern normal cycle of sleep and wakefulness implies that, at Biology . New York : Globe Book Company, 1969 : 170 ) . specific times , various neural systems are being activated US 2020/0368491 A1 Nov. 26 , 2020 2 while others are being turned off. A key to the neurobiology Heart rate variability , well - known to increase during REM , of sleep is , therefore , to understand the various stages of also correlates inversely with delta - wave oscillations over sleep . In 1953 , Nathaniel Kleitman and Eugene Aserinksy the 90 -minute cycle . showed , using electroencephalographic ( EEG ) recordings [ 0016 ) Homeostatic functions, especially thermoregula from normal human subjects, that sleep comprises different tion , normally occur during non - REM sleep , but not during stages that occur in a characteristic sequence . REM sleep . During REM sleep , body temperature tends to [ 0012 ] Humans descend into sleep in stages that succeed drift from its mean level, and during non - REM sleep , to each other over the first hour or so after retiring. These return to normal . The alternation between the stages , there characteristic stages are defined primarily by electroen fore , maintains body temperature within an acceptable cephalographic criteria . Initially , during " drowsiness , ” the range . frequency spectrum of the electroencephalogram ( EEG ) is [ 0017 ] In humans, the transition between non -REM and shifted toward lower values , and the amplitude of the REM is abrupt; in other animals , less so . cortical waves slightly increases. This drowsy period , called [ 0018 ] Different models have been proposed to elucidate stage I sleep , eventually gives way to light or stage II sleep , the complex rhythm of electrochemical processes that result which is characterized by a further decrease in the frequency in the regular alternation of REM and non - REM sleep . of the EEG waves and an increase in their amplitude , Monoamines are active during non - REM stages but not together with intermittent high - frequency spike dusters during REM stages , whereas acetylcholine is more active called sleep spindles . Sleep spindles are periodic bursts of during REM sleep . The reciprocal interaction model pro activity at about 10-12 Hz that generally last 1 or 2 seconds posed in the 1970s suggested a cyclic give and take between and arise as a result of interactions between thalamic and these two systems . More recent theories such as the " flip cortical neurons . In stage III sleep , which represents mod flop ” model proposed in the 2000s include the regulatory erate to deep sleep , the number of spindles decreases, role of in inhibitory neurotransmitter gamma -aminobutyric whereas the amplitude of low - frequency waves increases acid (GABA ). still more . In the deepest level of sleep , stage N sleep , the [ 0019 ] The average length of the sleep cycle in an adult predominant EEG activity consists of low - frequency ( 1-4 man is 90 minutes. N1 (NREM stage 1 ) is when the person Hz ) , high - amplitude fluctuations called delta waves , the is drowsy or awake to falling asleep . Brain waves and characteristic slow waves for which this phase of sleep is muscle activity start to decrease at this stage . N2 is when the named . The entire sequence from drowsiness to deep stage person experiences a light sleep . Eye movement has stopped N sleep usually takes about an hour. by this time . Brain wave frequency and muscle tonus is [ 0013 ] These four sleep stages are called non - rapid eye decreased. The heart rate and body temperature go down . N3 movement ( non - REM or NREM ) sleep , and its most promi or even N4 is the most difficult stages to be awakened . Every nent feature is the slow - wave ( stage IV ) sleep . Sometimes , part of the body is now relaxed , breathing is slowed , blood stages III and IV are combined and referred to jointly as the pressure and body temperature are reduced . REM sleep is a stage III sleep . It is most difficult to awaken people from unique state , in which dreams usually occur. The brain is slow - wave sleep ; hence , it is considered to be the deepest awake , and body paralyzed . This unique stage is usually stage of sleep . Following a period of slow - wave sleep , when the person is in the deepest stage of sleep and dreams. however, EEG recordings show that the stages of sleep The average length of a sleep cycle usually thought of as 90 reverse to reach a quite different state called rapid eye min . Some sources give it 90-110 minutes or an even wider movement, or REM , sleep . In REM sleep , the EEG record range of 80-120 minutes . A seven - eight -hour sleep usually ings are remarkably similar to that of the awake state . This includes five cycles , the middle two of which tend to be mode is bizarre : a dreamer's brain becomes highly active longer. REM takes up more of the cycle as the night goes on . while the body's muscles are paralyzed , and breathing and [ 0020 ] When falling asleep , a series of highly orchestrated heart rate become erratic . After about 10 minutes in REM events puts the brain to sleep in the above -mentioned stages . sleep , the brain typically cycles back through the non - REM Technically, sleep starts in the brain areas that produce sleep stages . Slow - wave sleep usually occurs again in the slow - wave sleep (SWS ) . It has been shown that two groups second period of this continual cycling , but not during the of cells — the ventrolateral preoptic nucleus in the hypothala rest of the night. On average , four additional periods of REM mus and the parafacial zone in the brain stem are involved sleep occur, each having longer than the preceding cycle in prompting SWS . When these cells are activated , it triggers durations. a loss of consciousness . After SWS , REM sleep begins . The [ 0014 ] The sleep cycle is an oscillation between the non purpose of REM sleep remains a biological mystery , despite REM ( including slow - waves ) and REM phases of sleep . It our growing understanding of its biochemistry and neuro is sometimes called the ultradian sleep cycle , sleep - dream biology . It has been shown that a small group of cells in the cycle , or REM - NREM cycle , to distinguish it from the brain stem , called the subcoeruleus nucleus, control REM circadian alternation between sleep and wakefulness . In sleep. When these cells become injured or diseased , people humans, this cycle takes on average between land 2 hours do not experience the muscle paralysis associated with REM ( approximately 90 min ). sleep , which can lead to REM sleep behavior disordera [ 0015 ] The timing of sleep cycles can be observed on EEG serious condition in which the afflicted violently act out their by marked distinction in brainwaves manifested during dreams. For reasons that are not clear, the amount of REM REM and non -REM sleep . Delta wave activity , correlating sleep each day decreases from about 8 hours at birth to 2 with slow - wave ( deep ) sleep , in particular, shows regular hours at 20 years , to only about 45 minutes at 70 years of oscillations throughout a night's sleep . Secretions of various age . See Mallick , B. N .; S. R. Pand - Perumal; RobedW , hormones , including renin , growth hormone , and prolactin , McCarley ; and Adrian R. Morrison ( 2011 ) . Rapid Eye correlate positively with delta - wave activity, whereas secre Movement Sleep : Regulation and Function . Cambridge Uni tion of thyroid - stimulating hormone correlates inversely. versity Press . ISBN 978-0-521-11680-0 ; Nir, and Tononi, US 2020/0368491 A1 Nov. 26 , 2020 3

“ Dreaming and the Brain : from Phenomenology to Neuro tomography ( “ PET ” ), near - infrared spectroscopy ( “ NIRS ” ), physiology . ” Trends in Cognitive Sciences , vol . 14 , no . 2 , single - photon emission computed tomography ( “ SPECT” ), 2010 , pp . 88-100 ; and Varela, F. , Engel , J. , Wallace , B. , & and others . Noninvasive neuromodulation technologies have Thupten , lip . ( 1997 ) . Sleeping, dreaming, and dying: An also been developed that can modulate the pattern of neural exploration of consciousness with the Dalai Lama . activity, and thereby cause altered behavior, cognitive states, [ 0021 ] Mental State : A mental state is a state of mind thata perception , and motor output. Integration of noninvasive subject is in . Some mental states are pure and unambiguous, measurement and neuromodulation techniques for identify while humans are capable of complex states that are a ing and transplanting brain states from neural activity would combination of mental representations, which may have in be very valuable for clinical therapies, such as brain stimu their pure state contradictory characteristics. There are sev lation and related technologies often attempting to treat eral paradigmatic states ofmind thata subject has : love , hate , disorders of cognition . See , Mehmetali Gülpinar, Berrak C pleasure , fear, and pain . Mental states can also include a Ye?en , “ The Physiology of Learning and Memory : Role of waking state , a sleeping state , a flow ( or being in the Peptides and Stress ” , Current Protein and Peptide Science , “ zone” ), and a mood ( a mental state ) . A mental state is a 2004 ( 5 ); www.researchgate.net/publication/8147320_The_ hypothetical state that corresponds to thinking and feeling Physiology_of_Learning_and_Memory_Role_of_Peptides and consists of a conglomeration of mental representations. and_Stress . Deep brain stimulation is described in NIH A mental state is related to an emotion , though it can also Research Matters, “ A noninvasive deep brain stimulation relate to cognitive processes. Because the mental state itself technique ” , ( 2017 ) ; Brainworks, “ QEEG Brain Mapping ” ; is complex and potentially possess inconsistent attributes, and Carmon , A. , Mor, J. , & Goldberg, J. ( 1976 ) . Evoked clear interpretation of mental state through external analysis cerebral responses to noxious thermal stimuli in humans. ( other than self - reporting ) is difficult or impossible . How Experimental Brain Research , 25 ( 1 ) , 103-107 . ever, some studies repod that certain attributes of mental state or thought processes may , in fact, be determined [ 0025 ] Mental State : A number of studies repod that through passive monitoring, such as EEG , or fMRI with certain attributes of mental state or thought processes may , some degree of statistical reliability . In most studies, the in fact, be determined through passive monitoring , such as characterization of mental state was an endpoint, and the raw EEG , with some degree of statistical reliability. In most signals, after statistical classification or semantic labeling, studies, the characterization of mental state was an endpoint, are superseded. The remaining signal energy treated as and the raw signals , after statistical classification or semantic noise . Current technology does not permit a precise abstract labeling , are superseded and the remaining signal energy encoding or characterization of the full range of mental treated as noise . states based on neural correlates of mental state . [ 0026 ] Neural Correlates: A neural correlate of a sleep [ 0022 ] Brain : The brain is a key part of the central nervous state is an electro -neuro -biological state or the state assumed system , enclosed in the skull . In humans, and mammals by some biophysical subsystem of the brain , whose presence more generally, the brain controls both autonomic processes , necessarily and regularly correlates with such specific sleep as well as cognitive processes. The brain ( and to a lesser states . All properties credited to the mind , including con extent, the spinal cord ) controls all volitional functions of sciousness , emotion , and desires are thought to have direct the body and interprets information from the outside world . neural correlates . Neural correlates of a sleep state can be Intelligence, memory , emotions , speech , thoughts, move defined as the minimal set of neuronal oscillations that ments, and creativity are controlled by the brain . The central correspond to the given sleep stage . nervous system also controls autonomic functions and many [ 0027 ] Brainwaves : At the root of all our thoughts, emo homeostatic and reflex actions , such as breathing , heart rate , tions , and behaviors is the communication between neurons etc. The human brain consists of the cerebrum , cerebellum , within our brains, a rhythmic or repetitive neural activity in and brainstem . The brainstem includes the midbrain , the the central nervous system . The oscillation can be produced pons , and the medulla oblongata . Sometimes the diencepha by a single neuron or by synchronized electrical pulses from lon , the caudal part of the forebrain , is included . ensembles of neurons commmunicating with each other. The [ 0023 ] The brainstem has many basic functions, including interaction between neurons can give rise to oscillations at heart rate , breathing, sleeping , and eating. The skull imposes a different frequency than the firing frequency of individual a barrier to electrical access to the brain functions, and in a neurons. The synchronized activity of large numbers of healthy human , breaching the dura to access the brain is neurons produces macroscopic oscillations , which can be highly disfavored . The result is that electrical readings of observed in an electroencephalogram . They are divided into brain activity are filtered by the dura, the cerebrospinal fluid , bandwidths to describe their purported functions or func the skull , the scalp , skin appendages ( e.g., hair ), resulting in tional relationships. Oscillatory activity in the brain is a loss of potential spatial resolution and amplitude of signals widely observed at different levels of organization and is emanating from the brain . While magnetic fields resulting thought to play a key role in processing neural information . from brain electrical activity are accessible , the spatial Numerous experimental studies support a functional role of resolution using feasible sensors is also limited . neural oscillations . A unified interpretation , however, is still [ 0024 ] Technological advances now allow for the non not determined . Neural oscillations and synchronization invasive recording of large quantities of information from have been linked to many cognitive functions such as the brain at multiple spatial and temporal scales . Examples information transfer, perception , motor control, and include electroencephalogram ( “ EEG ” ) data using mufti memory . Electroencephalographic ( EEG ) signals are rela channel electrode arrays placed on the scalp or inside the tively easy and safe to acquire , have a long history of brain , magnetoencephalography ( “ MEG ” ), magnetic reso analysis, and can have high dimensionality, e.g. , up to 128 nance imaging ( “ MRI” ), functional data using functional or 256 separate recording electrodes. While the information magnetic resonance imaging ( “ fMRI” ), positron emission represented in each electrode is not independent of the US 2020/0368491 A1 Nov. 26 , 2020 4 others, and the noise in the signals high , there is much [ 0032 ] Scalp potential may be expressed as a volume information available through such signals that has not been integral of dipole moment per unit volume over the entire fully characterized to date . brain provided P ( r, t) is defined generally rather than in columnar terms. For the important case of dominant cortical [ 0028 ] Brainwaves have been widely studied in neural sources, scalp potential may be approximated by the fol activity generated by large groups of neurons, mostly by lowing integral over the cortical volume 0 , VS ( r, t ) =MOG (r , EEG . In general, EEG signals reveal oscillatory activity r ') .P (r ', t) do ( r' ) . If the volume element de ( r' ) is defined in ( groups of neurons periodically firing in synchrony ), in terms of cortical columns , the volume integral may be specific frequency bands: alpha ( 7.5-125 Hz ) that can be reduced to an integral over the folded cortical surface . The detected from the occipital lobe during relaxed wakefulness time- dependence of scalp potential is the weighted sum of and which increases when the eyes are closed ; delta ( 1-4 all dipole time variations in the brain , although deep dipole Hz ) , theta ( 4-8 Hz ) , beta ( 13-30 Hz ) , low gamma ( 30-70 volumes typically make negligible contributions. The vector Hz ) , and high gamma ( 70-150 Hz ) frequency bands, where Green's function G (r ,r ' ) contains all geometric and conduc faster rhythms such as gamma activity have been linked to tive information about the head volume conductor and cognitive processing. Higher frequencies imply multiple weights the integral accordingly. Thus, each scalar compo groups of neurons firing in coordination , either in parallel or nent of the Green's function is essentially an inverse elec in series, or both , since individual neurons do not fire at rates trical distance between each source component and scalp of 100 Hz . Neural oscillations of specific characteristics location . For the idealized case of sources in an infinite have been linked to cognitive states , such as awareness and medium of constant conductivity , the electrical distance consciousness and different sleep stages . See , Chang - Hwan equals the geometric distance . The Green's function Im , Computational EEG Analysis: Methods and Applica accounts for the tissue's finite spatial extent and its in tions ( Biological and Medical Physics, Biomedical Eng . ), homogeneity and anisotropy. The forward problem in EEG Sep. 11 , 2019 . consists of choosing a head model to provide G ( r, r' ) and [ 0029 ] Nyquist Theorem states that the highest frequency carrying out the integral for some assumed source distribu that can be accurately represented is one - haft of the sam tion . The inverse problem consists of using the recorded pling rate . Practically, the sampling rate should be ten times scalp potential distribution VS ( r ,t ) plus some constraints higher than the highest frequency of the signal . ( See , www . ( usual assumptions) on P ( r, t) to find the best fit source slideshare.net/ertyk/eeg-examples ). While EEG signals are distribution P?r, t ). Since the inverse problem has no unique largely band limited , the superimposed noise may not be . solution , any inverse solution depends critically on the Further, the EEG signals themselves represent components chosen constraints , for example, only one or two isolated from a large number of neurons , which fire independently. sources , distributed sources confined to the cortex , or spatial Therefore, large bandwidth signal acquisition may have and temporal smoothness criteria . High - resolution EEG uses utility. the experimental scalp potential VS ( r ,t ) to predict the poten [ 0030 ] It is a useful analogy to think of brainwaves as tial on the dura surface ( the unfolded membrane surrounding musical notes . Like in a symphony, the higher and lower the cerebral cortex ) VD ( r , t ) . This may be accomplished frequencies link and cohere with each other through har using a head model Green's function G (r ,r ' ) or by estimating monics , especially when one considers that neurons may be the surface Laplacian with either spherical or 3D splines . coordinated not only based on transitions, but also on phase These two approaches typically provide very similar dura delay. Oscillatory activity is observed throughout the central potentials VD (r ,t ) ; the estimates of dura potential distribu nervous system at all levels of organization . The dominant tion are unique subject to head model , electrode density, and neuro oscillation frequency is associated with a respective noise issues . mental state . The functions of brainwaves are wide - ranging [ 0033 ] In an EEG recording system , each electrode is and vary for different types of oscillatory activity. Neural connected to one input of a differential amplifier ( one oscillations also play an important role in many neurological amplifier per pair of electrodes ); a common system reference disorders . electrode ( or synthesized reference ) is connected to the other [ 0031 ] In standard EEG recording practice , 19 recording input of each differential amplifier. These amplifiers amplify electrodes are placed uniformly on the scalp ( the Interna the voltage between the active electrode and the reference tional 10-20 System ). In addition , one or two reference ( typically 1,000-100,000 times , or 60-100 dB of voltage electrodes (often placed on earlobes ) and a ground electrode gain ) . The amplified signal is digitized via an analog - to ( often placed on the nose to provide amplifiers with refer digital converter, after being passed through an anti- aliasing ence voltages ) are required . However, additional electrodes filter. Analog - to - digital sampling typically occurs at 256-512 may add minimal useful information unless supplemented Hz in clinical scalp EEG ; sampling rates of up to 20 kHz are by computer algorithms to reduce raw EEG data to a used in some research applications. The EEG signals can be manageable form . When large numbers of electrodes are captured with open source hardware such as OpenBCI , and employed , the potential at each location may be measured the signal can be processed by freely available EEG soft with respect to the average of all potentials ( the common ware such as EEGLAB or the Neurophysiological Bio average reference ), which often provides a good estimate of marker Toolbox . A typical adult human EEG signal is about potential at infinity . The common average reference is not 10 uV to 100 uV ( scalp ) and about 10-20 mV ( subdural appropriate when electrode coverage is sparse ( perhaps less electrodes ). than 64 electrodes ). See , Paul L Nunez and Ramesh Srini [ 0034 ] Delta wave (en.wikipedia.org/wiki/Delta_wave ) is vasan ( 2007 ) Electroencephalogram . Scholarpedia , 2 ( 2 ) : the frequency range up to 4 Hz . It tends to be the highest in 1348 , scholarpedia.org/article/Electroencephalogram . amplitude and the slowest waves . It is normally seen in Dipole localization algorithms may be useful to determine adults in NREM ( en.wikipedia.org/wiki/NREM ). It is also spatial emission patterns in EEG . seen normally in babies . It may occur focally with subcor US 2020/0368491 A1 Nov. 26 , 2020 5 tical lesions and in general distribution with diffuse lesions , frequencies for stimulation via sound ran into problems metabolic encephalopathy hydrocephalus or deep midline associated with infrasound, defined as any sound below 20 lesions . It is , usually, most prominent frontally in adults Hz frequency. Firstly, it is difficult to generate infrasound ( e.g. , FIRDA - frontal intermittent rhythmic delta ) and pos through acoustic speakers. Earbuds are too small for that and teriorly in children ( e.g. , OIRDA - occipital intermittent so are most regular speakers. Specialized large subwoofers rhythmic delta ). with circular design or sound guides may be used , but tend [ 0035 ] Theta is the frequency range from 4 Hz to 7 Hz . to be impractical. Theta is normally seen in young children . It may be seen in [ 0041 ] Binaural beats : ( See en.wikipedia.org/wiki/Beat_ drowsiness or arousal in older children and adults ; it can also (acoustics )#Binaural_beats ) A binaural beat is an auditory be seen in meditation . Excess theta for age represents illusion perceived when two different pure - tone sine waves , abnormal activity . It can be seen as a focal disturbance in both with frequencies lower than 1500 Hz , with less than a focal subcortical lesions ; it can be seen in the generalized 40 Hz difference between them , are presented to a listener distribution in diffuse disorder or metabolic encephalopathy dichotically ( one through each ear ). A more serious problem or deep midline disorders or some instances of hydrocepha is the effect of the infrasound on human health . While many lus . On the contrary, this range has been associated with animals ( e.g. , elephants and wales ) communicate via infra reports of relaxed , meditative , and creative states . sound , in humans, infrasound causes undesirable effects [ 0036 ] Alpha is the frequency range from 7 Hz to 14 Hz . including send of panic, fear, and anxiety . Prolonged expo This was the " posterior basic rhythm ” ( also called the sure to infrasound could be dangerous to human health . See , “ posterior dominant rhythm ” or the “ posterior alpha for example, Persinger, M. A. Nat Hazards ( 2014 ) 70 : rhythm ” ), seen in the posterior regions of the head on both 501.doi.org/10.1007/s11069-013-0827-3 . These problems sides , higher in amplitude on the dominant side . It emerges are addressed using binaural beats . For example, if a 530 Hz with the closing of the eyes and with relaxation and attenu pure tone is presented to a subject's right ear , while a 520 Hz ates with eye opening or mental exertion . The posterior basic pure tone is presented to the subject's left ear, the listener rhythm is slower than 8 Hz in young children ( therefore will perceive the auditory illusion of a third tone , in addition technically in the theta range ). In addition to the posterior to the two pure - tones presented to each ear. The third sound basic rhythm , there are other normal alpha rhythms such as is called a binaural beat , and in this example would have a the sensorimotor, or mu rhythm ( alpha activity in the con perceived pitch correlating to a frequency of 10 Hz , that tralateral sensory and motor cortical areas ) that emerges being the difference between the 530 Hz and 520 Hz pure when the hands and arms are idle ; and the “ third rhythm ” tones presented to each ear . ( alpha activity in the temporal or frontal lobes ) . Alpha can [ 0042 ] Binaural- beat perception originates in the inferior be abnormal; for example , an EEG that has diffuse alpha colliculus of the midbrain and the superior olivary complex occurring in a coma and is not responsive to external stimuli of the brainstem , where auditory signals from each ear are is referred to as “ alpha coma. ” integrated and precipitate electrical impulses along neural [ 0037 ] Beta is the frequency range from 15 Hz to about 30 pathways through the reticular formation up the midbrain to Hz . It is usually seen on both sides in symmetrical distri the thalamus, auditory cortex , and other cortical regions. bution and is most evident frontally. Beta activity is closely Binaural beats are widely used in brain stimulation . linked to motor behavior and is generally attenuated during [ 0043 ] EEG AND QEEG : An EEG electrode will mainly active movements . Low - amplitude beta with multiple and detect the neuronal activity in the brain region just beneath varying frequencies is often associated with active , busy , or it . However, the electrodes receive the activity from thou anxious thinking and active concentration . Rhythmic beta sands of neurons. One square millimeter of cortex surface , with a dominant set of frequencies is associated with various for example , has more than 100,000 neurons . It is only when pathologies, such as Dup15q syndrome, and drug effects, the input to a region is synchronized with electrical activity especially benzodiazepines . It may be absent or reduced in occurring at the same time that simple periodic waveforms areas of cortical damage . It is the dominant rhythm in in the EEG become distinguishable . The temporal pattern patients who are alert or anxious or who have their eyes associated with specific brainwaves can be digitized and open . encoded a non - transient memory , and embodied in or ref [ 0038 ] Gamma is the frequency range of approximately erenced by , computer software . 30-100 Hz . Gamma rhythms are thought to represent bind [ 0044 ] EEG ( electroencephalography ) and MEG (magne ing of different populations of neurons together into a toencephalography ) are available technologies to monitor network to carry out a certain cognitive or motor function . brain electrical activity . Each generally has sufficient tem [ 0039 ] Mu range is 8-13 Hz and partly overlaps with other poral resolution to follow dynamic changes in brain electri frequencies . It reflects the synchronous firing of motor cal activity . Electroencephalography ( EEG ) and quantitative neurons in a rest state . Mu suppression is thought to reflect electroencephalography ( EEG ) are electrophysiological motor mirror neuron systems because when an action is monitoring methods that analyze the electrical activity of the observed , the pattern extinguishes, possibly because of the brain to measure and display patterns that correspond to normal neuronal system and the mirror neuron system ago cognitive states and / or diagnostic information . It is typically out of sync” and interfere with each other . ( en.wikipedia . noninvasive, with the electrodes placed on the scalp , org /wiki / Electroencephalography ). See Reference List Table although invasive electrodes are also used in some cases . 2 . EEG signals may be captured and analyzed by a mobile [ 0040 ] All sleep stages are associated with frequencies device, often referred to as “ brain wearables. ” There are a below 13 Hz - delta ( 1-4 Hz ) , theta ( 4-8 Hz ) , and alpha ( 8-12 variety of “ brain wearables ” readily available on the market Hz ) . While these frequencies may be reproduced in tran today. EEGs can be obtained with a non - invasive method scranial electric ( or magnetic ) stimulation , or via sensory where the aggregate oscillations of brain electric potentials stimulation with light, any attempts to reproduce these are recorded with numerous electrodes attached to the scalp US 2020/0368491 A1 Nov. 26 , 2020 6 of a person . Most EEG signals originate in the brain's outer hyperpolarizing effects between action potentials. In myeli layer ( the cerebral cortex ), believed largely responsible for nated tracts , the current flows at the segments tend to be our thoughts, emotions , and behavior. Cortical synaptic small , and therefore , the signals from individual cells are action generates electrical signals that change in the 10 to small . Therefore, the largest signal components are from the 100 - millisecond range . Transcutaneous EEG signals are synapses and cell bodies. In the cerebrum and cerebellum , limited by the relatively insulating nature of the skull these structures are mainly in the cortex , which is largely surrounding the brain , the conductivity of the cerebrospinal near the skull, making electroencephalography useful, since fluid and brain tissue , relatively low amplitude of individual it provides spatial discrimination based on electrode loca cellular electrical activity, and distances between the cellular tion . However, deep signals are attenuated and poorly local current flows and the electrodes. EEG is characterized by : ized. Magnetoencephalography detects dipoles, which ( 1 ) Voltage ; ( 2 ) Frequency ; ( 3 ) Spatial location ; ( 4 ) Inter derive from current flow , rather than voltage changes. In the hemispheric symmetries; ( 5 ) Reactivity (reaction to state case of a radially or spherically symmetric current flow change ); ( 6 ) Character of waveform occurrence ( random , within a short distance , the dipoles will tend to cancel, while serial, continuous ); and ( 7 ) Morphology of transient events . net current flows long axons will reinforce . Therefore , an EEGs can be separated into two main categories . Sponta electroencephalogram reads a different signal than a mag neous EEG which occur in the absence of specific sensory netoencephalogram . stimuli and evoked potentials ( EPs ) which are associated [ 0048 ] EEG - based studies of emotional specificity at the with sensory stimuli like repeated light flashes, auditory single - electrode level demonstrated that asymmetric activity tones , finger pressure , or mild electric shocks . The latter is at the frontal site , especially in the alpha ( 8-12 Hz ) band , is recorded , for example , by time averaging to remove effects associated with emotion . Voluntary facial expressions of of spontaneous EEG . Non - sensory triggered potentials are smiles of enjoyment produce higher left frontal activation . also known . EP's typically are time synchronized with the Decreased left frontal activity is observed during the vol trigger, and thus have an organization principle . Event untary facial expressions of fear. In addition to alpha -band related potentials ( ERPs ) provide evidence of a direct link activity, theta band power at the frontal midline ( Fm ) has between cognitive events and brain electrical activity in a also been found to relate to emotional states . Pleasant ( as wide range of cognitive paradigms. It has generally been opposed to unpleasant) emotions are associated with an held that an ERP is the result of a set of discrete stimulus increase in frontal midline theta power. Many studies have evoked brain events . Event - related potentials ( ERPs ) are sought to utilize pattern classification , such as neural net recorded in the same way as EPs , but occur at longer works, statistical classifiers , clustering algorithms, etc., to latencies from the stimuli and are more associated with an differentiate between various emotional states reflected in endogenous brain state . EEG . Ekman and Davidson found that voluntary facial [ 0045 ] Typically , a magnetic sensor with sufficient sensi expressions of smiles of enjoyment produced higher left tivity to individual cell depolarization or small groups is a frontal activation ( Ekman P , Davidson RJ ( 1993 ) Voluntary superconducting quantum interference device ( SQUID ), Smiling Changes Regional Brain Activity. Psychol Sci 4 : which requires cryogenic temperature operation , either at 342-345 ) . Another study by Coan et al . found decreased left liquid nitrogen temperatures ( high -temperature supercon frontal activity during the voluntary facial expressions of ductors, HIS ) or liquid helium temperatures ( low - tempera fear ( Coan JA , Allen J J , Harmon - Jones E ( 2001 ) Voluntary ture superconductors, LTS ). However, current research facial expression and hemispheric asymmetry over the fron shows the possible feasibility of room temperature super tal cortex . Psychophysiology 38 : 912-925 ) . Sammler and conductors ( 20 C ) . Magnetic sensing has an advantage, due colleagues , for example, showed that pleasant ( as opposed to to the dipole nature of sources , of having better potential unpleasant) emotion is associated with an increase in frontal volumetric localization ; however , due to this added infor midline theta power ( Sammler D , Grigutsch M , Fritz T , mation , the complexity of signal analysis is increased . Koelsch 5 ( 2007 ) Music and emotion : Electrophysiological [ 0046 ] In general, the electromagnetic signals detected correlates of the processing of pleasant and unpleasant represent action potentials, an automatic response of a nerve music. Psychophysiology 44 : 293-304 ) . To further demon cell to depolarization beyond a threshold , which briefly strate whether these emotion - specific EEG characteristics opens conduction channels . The cells have ion pumps which are strong enough to differentiate between various emotional seek to maintain a depolarized state . Once triggered, the states , some studies have utilized a pattern classification action potential propagates along the membrane in two analysis approach . dimensions , causing a brief high level of depolarizing ion [ 0049 ] When filtering brainwave signals, it is often useful flow . There is a quiescent period after depolarization that extract features from the noisy signal , where the features generally prevents oscillation within a single cell . Since the may be identified and correlated signal components can also exon extends from the body of the neuron , the action be removed without distortion of the remaining components. potential will typically proceed along the length of the axon , While this can be directly employed when applied to a which terminates in a synapse with another cell . While direct feature of interest, this may also be employed to simplify the electrical connections between cells occur , often the axon brainwave signal for later analysis by removing character releases a neurotransmitter compound into the synapse, istic features of a distinct patters from those sought. Thus, which causes depolarization or hyperpolarization of the consideration of non - sleep states is useful. target cell . Indeed , the result may also be the release of a [ 0050 ] Detecting different emotional states by EEG may hormone or peptide , which may have a local or more distant be more appropriate using EEG - based functional connec effect . tivity. There are various ways to estimate EEG - based func [ 0047 ] The electrical fields detectable externally tend to tional brain connectivity : correlation , coherence, and phase not include signals which low - frequency signals, such as synchronization indices between each pair of EEG elec static levels of polarization , or cumulative depolarizing or trodes had been used . The assumption is theta higher cor US 2020/0368491 A1 Nov. 26 , 2020 7 relation map indicates a stronger relationship between two hemisphere. In contrast, happiness was associated with a signals. ( Brazier M A Casby J U ( 1952 ) Cross - correlation wider synchronization between the frontal and occipital and autocorrelation studies of electroencephalographic sites . potentials. Electroen din neuro 4 : 201-211 ) . Coherence gives [ 0052 ] Different connectivity indices are sensitive to dif information similar to correlation but also includes the ferent characteristics of EEG signals . Correlation is sensitive covariation between two signals as a function of frequency . to phase and polarity but is independent of amplitudes . ( Cantero J L , Atienza M , Salas R M , Gomez C M ( 1999 ) Changes in both amplitude and phase lead to a change in Alpha EEG coherence in different brain states : an electro coherence (Guevara MA , Corsi - Cabrera M ( 1996 ) EEG physiological index of the arousal level in human subjects. coherence or EEG correlation ? Intl Psychophysiol 23 : 145 153 ) . The phase synchronization index is only sensitive to a Neurosci lett 271 : 167-70 . ) The assumption is that higher change in phase (Lachaux J P , Rodriguez E , Martinerie J , correlation indicates a stronger relationship between two Varela H ( 1999 ) Measuring phase synchrony in brain sig signals. (Guevara MA , Corsi- Cabrera M ( 1996 ) EEG coher nals . Hum Brain Mapp 8 : 194-208 ) . A number of studies ence or EEG correlation ? Int J Psychophysiology 23 : 145 have tried to classify emotional states by means of recording 153 ; Cantero JL , Atienza M , Salas RM , Gomez CM ( 1999 ) and statistically analyzing EEG signals from the central Alpha EEG coherence in different brain states : an electro nervous systems. See , for example : Lin YP, Wang C H , Jung physiological index of the arousal level in human subjects . TP, Wu T L , leng S K , et al . ( 2010 ) EEG -Based Emotion Neurosci lett 271 : 167-70 ; Adler G , Brassen S , Jajcevic A Recognition in Music Listening . IEEE T Bio Med Eng ( 2003 ) EEG coherence in Alzheimer's dementia . J Neural 57 : 1798-1806 ; Murugappan M , Nagarajan R , Yaacob S Transm 110 : 1051-1058 ; Deeny S P , Hillman CH , Janelle C ( 2010 ) Classification of human emotion from EEG using M , Hatfield B D ( 2003 ) Cortico - cortical communication and discrete wavelet transform . J Biomed Sci Eng 3 : 390-396 ; superior performance in skilled marksmen : An EEG coher Murugappan M , Nagarajan R , Yaacob S ( 2011 ) Combining ence analysis. J Sport Exercise Psy 25 : 188-204 . ) Phase Spatial Filtering and Wavelet Transform for Classifying synchronization among the neuronal groups estimated based Human Emotions Using EEG Signals. J Med . Bio . Eng . on the phase difference between two signals is another way 31 : 45-51 ; Berkman E , Wong D K Guimaraes MP, Uy E T , to estimate the EEG -based functional connectivity among Gross J J , et al . ( 2004 ) Brain wave recognition of emotions brain areas . It is . ( Franaszczuk P J , Bergey G K ( 1999 ) An in EEG . Psychophysiology 41 : S71-571 ; Chanel G , Kronegg autoregressive method for the measurement of synchroni J , Grandjean D , Pun T ( 2006 ) Emotion assessment: Arousal zation of interictal and ictal EEG signals . Biol Cybern evaluation using EEG’s and peripheral physiological sig 81 : 3-9 . ) nals . Multimedia Content Representation , Classification and [ 0051 ] A number of groups have examined emotional Security 4105 : 530-537 ; Hagiwara KIAM ( 2003 ) A Feeling specificity using EEG - based functional brain connectivity. Estimation System Using a Simple Electroencephalograph . For example, Shin and Park showed that when emotional IEEE International Conference on Systems, Man and Cyber states become more negative at high room temperatures , netics. 4204-4209 ; and You - Yun Lee and Shulan Hsieh correlation coefficients between the channels in temporal studied different emotional states by means of EEG - based and occipital sites increase ( Shin ) -H , Park D - H . ( 2011 ) functional connectivity patterns. They used emotional film Analysis for Characteristics of Electroencephalogram dips to elicit three different emotional states . ( EEG ) and Influence of Environmental Factors According to [ 0053 ] The dimensional theory of emotion , which asserts Emotional Changes. In Lee G , Howard D , Slczak D , editors . that there are neutral, positive , and negative emotional Convergence and Hybrid Information Technology. Springer states , may be used to classify emotional states because Berlin Heidelberg , 488-500 . ) Hinrichs and Machleidt dem numerous studies have suggested that the responses of the onstrated that coherence decreases in the alpha band during central nervous system correlate with emotional valence and sadness, compared to happiness (Hinrichs H , Machleidt W arousal. ( See , for example , Davidson R J ( 1993 ) Cerebral ( 1992 ) Basic emotions reflected in EEG - coherences . Intl Asymmetry and Emotion Conceptual and Methodological Psychophysiol 13 : 225-232 ) . Miskovic and Schmidt found Conundrums. Cognition Emotion 7 : 115-138 ; Jones N A , that EEG coherence between the prefrontal cortex and the Fox N A ( 1992 ) Electroencephalogram asymmetry during posterior cortex increased while viewing highly emotionally emotionally evocative films and its relation to positive and arousing ( i.e. , threatening ) images , compared to viewing negative affectivity . Brain Cogn 20 : 280-299 ; Schmidt L A , neutral images ( Miskovic V , Schmidt L A ( 2010 ) Cross Trainor U ( 2001 ) Frontal brain electrical activity ( EEG ) regional cortical synchronization during affective image distinguishes valence and ensity of musical emotions. viewing . Brain Res 1362 : 102-111 ) . Costa and colleagues Cognition Emotion 15 : 487-500 ; Tomarken A I , Davidson R applied the synchronization index to detect interaction in J , Henriques J B ( 1990 ) Resting frontal brain asymmetry different brain sites under different emotional states ( Costa predicts affective responses to films. J Pers Soc Psycho ) T , Rognoni E , Galati D ( 2006 ) EEG phase synchronization 59 : 791-801 . ) As suggested by Mauss and Robins ( 2009 ) , during emotional response to positive and negative film “ measures of emotional responding appear to be structured stimuli . Neurosci Lett 406 : 159-164 ) . Costa's results showed along dimensions ( e.g. , valence , arousal) rather than discrete an overall increase in the synchronization index among emotional states ( e.g. , sadness, fear, anger ) ". frontal channels during emotional stimulation , particularly [ 0054 ] EEG - based functional connectivity change was during negative emotion ( i.e. , sadness ). Furthermore , phase found to be significantly different among emotional states of synchronization patterns were found to differ between posi neutral, positive , or negative . Lee Y - Y , Hsieh S ( 2014 ) tive and negative emotions . Costa also found that sadness Classifying Different Emotional States by Means of EEG was more synchronized than happiness at each frequency Based Functional Connectivity Patterns . PLoS ONE 9 ( 4 ) : band and was associated with a wider synchronization both e95415 . doi.org/10.1371/journal.pone.0095415 . A connec between the right and left frontal sites and within the left tivity pattern may be detected by pattern classification US 2020/0368491 A1 Nov. 26 , 2020 8 analysis using Quadratic Discriminant Analysis. The results [ 0056 ] Using EEG to assess the emotional state has indicated that the classification rate was better than chance . numerous practical applications . One of the first such appli They concluded that estimating EEG - based functional con cations was the development of a travel guide based on nectivity provides a useful tool for studying the relationship emotions by measuring brainwaves by the Singapore tour between brain activity and emotional states . ism group . “ By studying the brainwaves of a family on vacation , the researchers drew up the Singapore Emotion [ 0055 ] Emotions affects learning. Intelligent Tutoring Sys Travel Guide , which advises future visitors of the emotions tems ( ITS ) learner model initially composed of a cognitive they can expect to experience at different attractions. ” module was extended to include a psychological module and (www.lonelyplanet.com/news/2017/04/12/singapore-emo an emotional module . Alicia Heraz et al . introduced an emomental agent. It interacts with an ITS to communicate tion -travel - guide ) Joel Pearson at University of New South the emotional state of the learner based upon his mental Wales and his group developed the protocol of measuring state . The mental state was obtained from the learner's brainwaves of travelers using EEG and decoding specific brainwaves . The agent learns to predict the learners emo emotional states . tions by using machine learning techniques . ( Alicia Heraz , [ 0057 ] Another recently released application pertains to Ryad Razaki; Claude Frasson , “ Using machine learning to virtual reality ( VR ) technology. On Sep. 18 , 2017 Looxid predict learner emotional state from brainwaves ” Advanced Labs launched a technology that harnesses EEG from a Learning Technologies, 2007. ICALT 2007. Seventh IEEE subject waring a VR headset . Looxid Labs intention is to International Conference on Advanced Learning Technolo factor in brainwaves into VR applications in order to accu gies ( ICALT 2007 ) ) See also : Ella T. Ma mpusti , Jose S. Ng , rately infer emotions. Other products such as MindMaze and Darren James I. Quinto , Grizelda L Teng, Merlin Teodosia even Samsung have tried creating similar applications C. Suarez, Rhia S. Trogo , “ Measuring Academic Affective through facial muscles recognition . ( scoffamyx.com/2017/ States of Students via Brainwave Signals ” , Knowledge and 10 / 13 / looxidlabs - vr - brain -waves - human -emotions / ). Systems Engineering ( KSE ) 2011 Third International Con According to its website ( looxidlabs.com/device-2/ ), the ference on , pp . 226-231 , 2011 ; Judith J. Azcarraga, John Looxid Labs Development Kit provides a VR headset Francis Ibanez Jr., Ianne Robert Lim , Nestor Lumanas Jr., embedded with miniaturized eye and brain sensors . It uses “ Use of Personality Profile in Predicting Academic Emotion 6 EEG channels : Fp1 , Fp2 , AF7 , AFB , AR , AF4 in the Based on Brainwaves Signals and Mouse Behavior ” , international 10-20 system . Knowledge and Systems Engineering ( KSE ) 2011 Third [ 0058 ] To assess a users state of mind , a computer may be International Conference on , pp . 239-244 , 2011 ; Y - Hung used to analyze the EEG signals produced by the brain of the Liu , Chien - Te Wu, Yung -Hwa Kao , Ya - Ting Chen , “ Single user . However, the emotional states of a brain are complex , trial EEG - based emotion recognition using kernel Eigen and the brainwaves associated with specific emotions seem emotion pattern and adaptive support vector machine ” , to change overtime. Wei -Long Zheng at Shanghai Liao Tong Engineering in Medicine and Biology Society ( EMBC ) University used machine learning to identify the emotional 2013 35th Annual International Conference of the IEEE , pp . brain states and to repeat it reliably . The machine learning 4306-4309 , 2013 , ISSN 1557-170X ; Thong Tri Vo , Nam algorithm found a set of patterns that clearly distinguished Phuong Nguyen, Toi Vo Van, IFMBE Proceedings, vol . 63 , positive , negative , and neutral emotions that worked for pp . 621 , 2018 , ISSN 1680-0737 , ISBN 978-981-10-4360-4 ; different subjects and for the same subjects over time with Adrian Rodriguez Aguinaga, Miguel Angel Lopez Ramirez , an accuracy of about 80 percent. ( See Wei- Long Zheng, Lecture Notes in Computer Science , vol . 9456 , pp . 177 , Jia - Yi Zhu, Bao - Liang Lu , Identifying Stable Patterns over 2015 , ISSN 0302-9743 , ISBN 978-3-319-26507-0 ; Judith Time for Emotion Recognition from EEG , arxiv.org/abs/ Azcarraga, Merlin Teodosia Suarez , “ Recognizing Student 1601.02197 ; see also How One Intelligent Machine Learned Emotions using Brainwaves and Mouse Behavior Data ” , to Recognize Human Emotions , MIT Technology Review , International Journa of Distance Education Technologies , Jan. 23 , 2016. ) vol . 11 , pp . 1 , 2013 , ISSN 1539-3100 ; OTri Thong Vo , [ 0059 ] MEG : Magnetoencephalography ( MEG ) is a func Phuong Nam Nguyen , Van Toi Vo, IFMBE Proceedings, vol . tional neuroimaging technique for mapping brain activity by 61 , pp . 67 , 2017 , ISSN 1680-0737 , ISBN 978-981-10-4219 recording magnetic fields produced by electrical currents 5 ; Alicia Heraz , Claude Frasson , Lecture Notes in Computer occurring naturally in the brain , using very sensitive mag Science , vol . 5535 , pp . 367 , 2009 , ISSN 0302-9743 , ISBN netometers . Arrays of SQUIDs (superconducting quantum 978-3-642-02246-3 ; Hamwira Yaacob , Wahab Abdul, interference devices) are currently the most common mag Norhaslinda Kamaruddin , “ Classification of EEG signals netometer, while the SERF ( spin exchange relaxation - free ) using MLP based on categorical and dimensional percep magnetometer is being investigated ( Hämäläinen , Matti; tions of emotions ” , Information and Communication Tech Han , Riiffa ; Ilmoniemi , Risto J .; Knuutila , Jukka; Lounas nology for the Muslim World ( ICT4M ) 2013 5th Interna maa , Olli V. ( 1993 ) . " Magnetoencephalography - theory, tional Conference on , pp . 1-6 , 2013 ; Yuan - Pin Lin , Chi instrumentation , and applications to noninvasive studies of Hong Wang, Tzyy - Ping Jung , Tien - Lin Wu, Shyh -Kang the working human brain ” . Reviews of Modern Physics. 65 Jeng, Jeng - Ren Duann , Jyh -Horng Chen , “ EEG - Based Emo ( 2 ) : 413-497 . ISSN 0034-6861 . doi: 10.1103 /RevModPhys . tion Recognition in Music Listening ”, Biomedical Engineer 65.413 . ) It is known that “ neuronal activity causes local ing IEEE Transactions on , vol . 57 , pp . 1798-1806 , 2010 , changes in cerebral blood flow , blood volume , and blood ISSN 0018-9294 ; Yi- Hung Liu , Wei - Teng Cheng, Yu - Tsung oxygenation ” ( Dynamic magnetic resonance imaging of Hsiao , Chien - Te Wu, Mu -Derleng , “ EEG - based emotion human brain activity during primary sensory stimulation . K recognition based on kernel Fishers discriminant analysis K Kwong, J. W. Belliveau , D. A. Chester, I. E. Goldberg , R. and spectral powers ” , Systems Man and Cybernetics ( SMC ) M. Weisskoff, B. P. Poncelet D. N. Kennedy, B. E. Hoppel , 2014 IEEE International Conference on , pp . 2221-2225 , M. S. Cohen , and R. Turner ). Using “ a 122 - channel D.C. 2014 . SQUID magnetometer with a helmet - shaped detector array US 2020/0368491 A1 Nov. 26 , 2020 9 covering the subjects head ” it has been shown that the ing state associated with creative insights ” , through the “ system allows simultaneous recording of magnetic activity facilitation of neural connectivity . Alpha - theta training has all over the head .” ( 122 - channel squid instrument for inves also been shown to improve novice singing in children . tigating the magnetic signals from the human brain . ) A. I. Alpha - theta neurofeedback , in conjunction with heart rate Ahonen , M. S. Ha malainen , M. J. Kajola , J. E. T. Knuutila , variability training, a form of biofeedback , has also pro P. P. Laine , O. V. Lounasmaa , L. T. Parkkonen , J. T. Simola , duced benefits in dance by enhancing performance in com and C. D. Tesche Physica Scripta , Volume 1993 , T49A ) . petitive ballroom dancing and increasing cognitive creativ [ 0060 ] In some cases , magnetic fields cancel, and thus the ity in contemporary dancers . Additionally, neurofeedback detectable electrical activity may fundamentally differ from has also been shown to instill a superior flow state in actors , the detectable electrical activity obtained via EEG . How possibly due to greater immersion while performing. ever , the main types of brain rhythms are detectable by both [ 0065 ] Several studies of brain wave activity in experts methods. See : U.S. Pat . Nos . 5,059,814 ; 5,118,606 ; 5,136 , while performing a task related to their respective area of 687 ; 5,224,203 ; 5,303,705 ; 5,325,862 ; 5,461,699 ; 5,522 , expertise revealed certain characteristic telltale signs of 863 ; 5,640,493 ; 5,715,821 ; 5,719,561 ; 5,722,418 ; 5,730 , so - called “ flow ” associated with top - flight performance . 146 ; 5,736,543 ; 5,737,485 ; 5,747,492 ; 5,791,342 ; 5,816 , Mihaly Csikszentmi ha lyi ( University of Chicago ) found 247 ; 6,497,658 ; 6,510,340 ; 6,654,729 ; 6,893,407 ; 6,950 , that the most skilled chess players showed less EEG activity 697 ; 8,135,957 ; 8,620,206 ; 8,644,754 ; 9,118,775 ; 9,179 , in the prefrontal cortex , which is typically associated with 875 ; 9,642,552 ; 20030018278 ; 20030171689 ; higher cognitive processes such as working memory and 20060293578 ; 20070156457 ; 20070259323 ; 20080015458 ; verbalization , during a game . See , Chris Berka et al . , 20080154148 ; 20080229408 ; 20100010365 ; 20100076334 ; Advanced Brain Monitoring, Carlsbad , EA The Interna 20100090835 ; 20120046531 ; 20120052905 ; 20130041281 ; tional Sport and Society , vol 1 , p 87 . 20150081299 ; 20150262016 . See EP1304073A2 ; [ 0066 ] Low Energy Neurofeedback System ( LENS ) : The EP1304073A3 ; W02000025668A1 ; and LENS , or Low Energy Neurofeedback System , uses a very W02001087153A1 . low power electromagnetic field , to carry feedback to the [ 0061 ] MEG seek to detect the magnetic dipole emission person receiving it . The feedback travels down the same from an electrical discharge in cells , e.g. , neural action wires carrying the brainwaves to the amplifier and computer. potentials. Typical sensors for MEGs are superconducting Although the feedback signal is weak , it produces a mea quantum interference devices ( SQUIDs ) . These currently surable change in the brainwaves without conscious effort require cooling to liquid nitrogen or liquid helium tempera from the individual receiving the feedback . The system is tures . However, the development of room temperature, or software controlled , to receive input from EEG electrodes, near room temperature superconductors, and miniature cryo to control the stimulation . Through the scalp . Neurofeed coolers, may permit field deployments and portable or back uses a feedback frequency that is different from , but mobile detectors. Because MEGs are less influenced by correlates with , the dominant brainwave frequency. When medium conductivity and dielectric properties, and because exposed to this feedback frequency, the EEG amplitude they inherently detect the magnetic field vector, MEG tech distribution changes in power . Most of the time , the brain nology permits volumetric mapping of brain activity and waves reduce in power , but at times they also increase in distinction of complementary activity that might suppress power. In either case , the result is a changed brainwave state detectable EEG signals. MEG technology also supports and much greater ability for the brain to regulate itself . See , vector mapping of fields, since magnetic emitters are inher Janice Chen , Content -Based Brainwave Analysis: Memories ently dipoles, and therefore a larger amount of information are not unique. Nature Neuroscience , DOI: 10.1038 /nn.4450 ; is inherently available . See , Reference List Table 3 . Andy Coghlan , “ Our brains record and remember things in [ 0062 ] EEGs and MEGs can monitor the state of con exactly the same way ” , New Scientist, Dec. 5 , 2016 (www . sciousness . For example, states of deep sleep are associated newscientistcom / adicle /2115093 - our- brains -record -and - re with slower EEG oscillations of larger amplitude. Various member - things - in -exactly - the -same - way / ); Brian Pasley, signal analysis methods allow for robust identifications of Frontiers in Neuroengineering , doi.org/whb; Helen Thom distinct sleep stages , depth of anesthesia , epileptic seizures , son , “ Hearing our inner voice ” . New Scientist, Oct. 29 , 2014 and connections to detailed cognitive events . (www.newscientistcom / article /mg22429934-000 -brain -de [ 0063 ] Neurofeedback : Neurofeedback ( NFB ) , also called coder - can - eavesdrop - on - your -inner - voice / ); Bernard Bal neurotherapy or neurobiofeedback , is a type of biofeedback leine , Proceedings of the National Academy of Sciences , that uses real - time displays of brain activity - most commonly DOI : 10.1073 / pnas.1113158108 ; Wendy Zukerman , “ Habits electroencephalography ( EEG ) , to teach self -regulation of form when brainwaves slow down ” , New Scientist, Sep. 26 , brain function . Typically, sensors are placed on the scalp to 2011 (www.newscientistcom /adicle / dn20964 -habits -form measure activity, with measurements displayed using video when - brainwaves -slow - down /) ; Smith , K Mind -reading displays or sound . The feedback may be in various other with a brain scan . Nature ( 2008 ) . oi.org/10.1038/news.2008. forms as well . Typically, the feedback is sought to be 650 ; Kay, K N. , Naselaris, T. , Prenger, R. J. Er Gallant, J. L presented through primary sensory inputs, but this is not a Nature advanced online publication doi :10.1038 /na limitation on the technique. ture06713 ( 5 Mar. 2008 ) ; Haynes, J.-D. et al . Current [ 0064 ] The applications of neurofeedback to enhance per Biology 17 , 323-328 ( 2007 ) ; Thorn , Catherine A. , Hisham formance extend to the ads in fields such as music , dance , Atallah , Mark Howe , and Ann M. Gray biel . “ Differential and acting . A study with conservatoire musicians found that dynamics of activity changes in dorsolateral and dorsome alpha - theta training benefitted the three music domains of dial striatal loops during learning . ” Neuron 66 , no . 5 ( 2010 ) : musicality , communication , and technique. Historically , 781-795 ; Howe , Mark W., Hisham E. Atallah , Andrew alpha - theta training, a form of neurofeedback , was created to McCool , Daniel J. Gibson , and Ann M. Gray biel . “ Habit assist creativity by inducing hypnagogia, a " borderline wak learning is associated with major shifts in frequencies of US 2020/0368491 A1 Nov. 26 , 2020 10 oscillatory activity and synchronized spike firing in stria method “ a system capable of identifying particular nodes in tum . ” Proceedings of the National Academy of Sciences an individual's brain , the firings of which affect character 108 , no . 40 ( 2011 ) : 16801-16806 ; Pawel Stepien , Wlodzimi istics such as appetite , hunger, thirst, communication skills ” erz Klonowski and Nikolay Suvorov, Nonlinear analysis of and " devices mounted to the person ( e.g. underneath the EEG in chess players , EPJ Nonlinear Biomedical Physics scalp ) may be energized in a predetermined manner or 20153 : 1 ; Junior, L. R. S. , Cesar, F. H. G. , Rocha , F. T. , and sequence to remotely cause particular identified brain node Thomaz , C. E. EEG and Eye Movement Maps of Chess ( s ) to be fired in order to cause a predetermined feeling or Players. Proceedings of the Sixth International Conference reaction in the individual ” without technical description of on Pattern Recognition Applications and Methods . implementation . This patent also describes, that “ brain activ ( ICPRAM 2017 ) pp . 343-441 . ( fei.edu.br/cet/icpram17_ ity [ is monitored ] by way of electroencephalograph ( EEG ) Laerciolunior.pdf ); You - Yun Lee , Shulan Hsieh . Classifying methods, magnetoencephalograph ( MEG ) methods, and the Different Emotional States by Means of EEG - Based Func like . ” For example, see U.S. Pat . Nos . 5,816,247 and 5,325 , tional Connectivity Patterns . Apr. 17 , 2014 , ( doi.org/10. 862. See also Reference List Table 5 . 1371/ journal.pone.0095415 ) . U.S. Pat . No. 9,763,592 pro [ 0069 ] Brain Entrainment: Brain entrainment, also vides a system for instructing a user behavior change referred to as brainwave synchronization and neural entrain comprising: collecting and analyzing bioelectrical signal ment, refers to the capacity of the brain to naturally syn datasets; and providing a behavior change suggestion based chronize its brainwave frequencies with the rhythm of uponthe analysis. A stimulus may be provided to prompt an periodic external stimuli, most commonly auditory, visual , action by the user , which may be visual , auditory, or haptic . or tactile . Brainwave entrainment technologies are used to See also U.S. Pat . No. 9,622,660 , 20170041699 ; induce various brain states , such as relaxation or sleep , by 20130317384 ; 20130317382 ; 20130314243 ; 20070173733 ; creating stimuli that occur at regular, periodic intervals to and 20070066914. Sensory Stimulation : Light, sound or mimic electrical cycles of the brain during the desired states, electromagnetic fields may be used to remotely convey a thereby “ training ” the brain to consciously alter states . temporal pattern of brainwaves. See Reference List Table 4 . Recurrent acoustic frequencies, flickering lights , or tactile [ 0067 ] Light Stimulation : The functional relevance of vibrations are the most common examples of stimuli applied brain oscillations in the alpha frequency range ( 8-13 Hz ) has to generate different sensory responses . It is hypothesized been repeatedly investigated through the use of rhythmic that listening to these beats of certain frequencies one can visual stimulation . There are two hypotheses on the origin of induce a desired state of consciousness that corresponds steady - state visual evoked potential ( SSVEP ) measured in with specific neural activity. Patterns of neural firing, mea EEG during rhythmic stimulation : entrainment of brain sured in Hz , correspond with alertness states such as focused oscillations and superposition of event - related responses attention , deep sleep , etc. ( ERPs ) . The entrainment but not the superposition hypoth [ 0070 ] Neural oscillations are rhythmic or repetitive elec esis justifies rhythmic visual stimulation as a means to trochemical activity in the brain and central nervous system . manipulate brain oscillations because superposition assumes Such oscillations can be characterized by their frequency, a linear summation of single responses, independent from amplitude, and phase . Neural tissue can generate oscillatory ongoing brain oscillations . Participants stimulated with the activity driven by mechanisms within individual neurons , as rhythmic flickering light of different frequencies and inten well as by interactions between them . They may also adjust sities , and entrainment was measured by comparing the frequency to synchronize with the periodic vibration of phase coupling of brain oscillations stimulated by rhythmic external acoustic or visual stimuli. The functional role of visual flicker with the oscillations induced by arrhythmic jilt neural oscillations is still not fully understood ; however, red stimulation , varying the time , stimulation frequency , and they have been shown to correlate with emotional responses , intensity conditions. Phase coupling was found to be more motor control, and a number of cognitive functions includ pronounced with increasing stimulation intensity as well as ing information transfer, perception, and memory . Specifi at stimulation frequencies closer to each participant’s intrin cally, neural oscillations, in particular theta activity, are sic frequency . Even in a single sequence of an SSVEP , extensively linked to memory function , and coupling nonlinear features intermittency of phase locking ) was between theta and gamma activity is considered to be vital found that contradict the linear summation of single for memory functions, including episodic memory . Electro responses , as assumed by the superposition hypothesis . encephalography ( EEG ) has been most widely used in the Thus , evidence suggests that visual rhythmic stimulation study of neural activity generated by large groups of neu entrains brain oscillations , validating the approach of rhyth rons, known neural ensembles, including in gations of mic stimulation as manipulation of brain oscillations. See , the changes that occur in electroencephalographic profiles Notbohm A , Kudhs J , Herrmann CS , Modification of Brain during cycles of sleep and wakefulness . EEG signals change Oscillations via Rhythmic Light Stimulation Provides Evi dramatically during sleep and show a transition from faster dence for Entrainment but Not for Superposition of Event frequencies to increasingly slower frequencies , indicating a Related Responses , Front Hum Neurosci . 2016 Feb. 3 ; relationship between the frequency of neural oscillations and 10:10 . doi: 10.3389 / fnhum.2016.00010.eCollection 2016. It cognitive states , including awareness and consciousness. is also known that periodic visual stimulation can trigger [ 0071 ] The term " entrainment ” has been used to describe epileptic seizures . a shared tendency of many physical and biological systems [ 0068 ] It is known to analyze EEG patterns to extract an to synchronize their periodicity and rhythm through inter indication of certain volitional activity ( U.S. Pat . No. 6,011 , action . This tendency has been identified as specifically 991 ) . This technique describes that an EEG recording can be pertinent to the study of sound and music generally, and matched against a stored normalized signal using a com acoustic rhythms specifically. The most ubiquitous and puter. This matched signal is then translated into the corre familiar examples of neuromotor entrainment to acoustic sponding reference . The patent application describes a stimuli are observable in spontaneous foot or finger tapping US 2020/0368491 A1 Nov. 26 , 2020 11 to the rhythmic beat of a song . Exogenous rhythmic entrain tional states . Mauss and Robinson , in their review paper , ment, which occurs outside the body, has been identified and have indicated that “ emotional state is likely to involve documented for a variety of human activities , which include circuits rather than any brain region considered in isolation " the way people adjust the rhythm of their speech patterns to (Mauss I B , Robinson MD ( 2009 ) Measures of emotion : A those of the subject with whom they communicate , and the review . Cogn Emot 23 : 209-237 . ) The amplitude, latency rhythmic unison of an audience dapping . Even among from the stimulus , and covariance ( in the case of multiple groups of strangers, the rate of breathing, locomotive , and electrode sites ) of each component can be examined in subtle expressive motor movements, and rhythmic speech connection with a cognitive task ( ERP ) or with no task ( EP ) . patterns have been observed to synchronize and entrain , in Steady - state visually evoked potentials ( 551 / EPs ) use a response to an auditory stimulus, such as a piece of music continuous sinusoidally -modulated flickering light, typically with a consistent rhythm . Furthermore , motor synchroniza superimposed in front of a TV monitor displaying a cogni tion to repetitive tactile stimuli occurs in animals , including tive task . The brain response in a narrow frequency band cats and monkeys as well as humans, with accompanying containing the stimulus frequency is measured . Magnitude, shifts in electroencephalogram ( EEG ) readings. Examples of phase , and coherence ( in the case of multiple electrode sites ) endogenous entrainment, which occurs within the body, may be related to different parts of the cognitive task . Brain include the synchronizing of human circadian sleep -wake entrainment may be detected through EEG or MEG activity . cycles to the 24 -hour cycle of light and dark , and the Brain entrainment may be detected through EEG or MEG frequency following response of humans to sounds and activity . See Reference List Table 8 . music. [ 0077 ] The entrainment hypothesis ( Thut and Miniussi , [ 0072 ] Brainwaves, or neural oscillations , share the fun 2009 ; Thut et al . , 2011a , 2012 ) , suggests the possibility of damental constituents with acoustic and optical waves , inducing a particular oscillation frequency in the brain using including frequency , amplitude, and periodicity. The syn an external oscillatory force ( e.g. , rTMS , but also tACS ) . chronous electrical activity of cortical neural ensembles can The physiological basis of oscillatory cortical activity lies in synchronize in response to external acoustic or optical the timing of the interacting neurons ; when groups of stimuli and also entrain or synchronize their frequency and neurons synchronize their firing activities , brain rhythms phase to that of a specific stimulus . Brainwave entrainment emerge , network oscillations are generated , and the basis for is a colloquialism for such ‘ neural entrainment' , which is a interactions between brain areas may develop ( Buzsaki, term used to denote the way in which the aggregate fre 2006 ) . Because of the variety of experimental protocols for quency of oscillations produced by the synchronous electri brain stimulation , limits on descriptions of the actual pro cal activity in ensembles of cortical neurons can adjust to tocols employed , and limited controls, consistency of synchronize with the periodic vibration of an external reported studies is lacking , and extrapolability is limited. stimuli , such as a sustained acoustic frequency perceived as Thus, while there is various consensus in various aspects of pitch , a regularly repeating pattern of intermittent sounds , the effects of extracranial brain stimulation , the results perceived as rhythm , or of a regularly rhythmically inter achieved have a degree of uncertainty dependent on details mittent flashing light. of implementation . On the other hand , within a specific [ 0073 ] Changes in neural oscillations , demonstrable experimental protocol, it is possible to obtain statistically through electroencephalogram ( EEG ) measurements , are significant and repeatable results . This implies that feedback precipitated by listening to music , which can modulate control might be effective to control implementation of the autonomic arousal ergotropically and trophotropically, stimulation for a given purpose ; however, studies that increasing and decreasing arousal respectively . Musical employ feedback control are lacking . auditory stimulation has also been demonstrated to improve [ 0078 ] Different cognitive states are associated with dif immune function , facilitate relaxation, improve mood , and ferent oscillatory patterns in the brain (Buzsaki , 2006 ; contribute to the alleviation of stress. Canopy and Knight 2010 ; Varela et al . , 2001 ) . Thut et al . [ 0074 ] The Frequency following response ( FFR ) , also ( 2011b ) directly tested the entrainment hypothesis by means referred to as Frequency Following Potential ( FFP ) , is a of a concurrent EEG - TMS experiment. They first deter specific response to hearing sound and music , by which mined the individual source of the parietal - occipital alpha neural oscillations adjust their frequency to match the modulation and the individual alpha frequency (magneto rhythm of auditory stimuli . The use of sound with intent to encephalography study ). They then applied rTMS at the influence cortical brainwave frequency is called auditory individual alpha power while recording the EEG activity at driving, by which frequency of neural oscillation is ‘ driven ' rest . The results confirmed the three predictions of the to entrain with that of the rhythm of a sound source . See entrainment hypothesis: the induction of a specific fre Reference List Table 6 . quency after TMS , the enhancement of oscillation during [ 0075 ] Baseline correction of event- related time- fre TMS stimulation due to synchronization, and phase align quency measure may be made by taking pre - event baseline ment of the induced frequency and the ongoing activity activity into consideration . A baseline period is defined by ( Thut et al . , 2011b ) . If associative stimulation is a general averaging values within a time window . Methods for base principle for human neural plasticity in which the timing and line correction in time - frequency analysis include various strength of activation are critical factors , it is possible that baseline value normalizations . The question of whether synchronization within or between areas using an external different emotional states are associated with specific pat force to phase / align oscillations can also favor efficient terns of physiological response has long being a subject of communication and associative plasticity ( or alter commu neuroscience research . See , Reference List Table 7 . nication ). In this respect associative , cortico - cortical stimu [ 0076 ] Electroencephalograms (EEG ) and functional lation has been shown to enhance the coherence of oscilla Magnetic Resonance Imaging , fMRI have been used to tory activity between the stimulated areas ( Plewnia et al . , study specific brain activity associated with different emo 2008 ) . In a coherence resonance (Longtin , 1997 ) , the addi US 2020/0368491 A1 Nov. 26 , 2020 12 tion of a certain amount of noise in an excitable system expected when TMS is frequency - tuned to the underlying results in the most coherent and proficient oscillatory brain oscillations ( Veniero et al . , 2011 ) . responses . The brain's response to external timing -embed [ 0082 ] Binaural Beats : Binaural beats are auditory brain ded stimulation can result in a decrease in phase variance stem responses which originate in the superior olivary and an enhanced alignment (clustering ) of the phase com nucleus of each hemisphere. They result from the interaction ponents of the ongoing EEG activity ( entraining, phase of two different auditory impulses , originating in opposite resetting ) that can change the signal - to -noise ratio and ears , below 1000 Hz and which differ in frequency between change signal efficacy. one and 30 Hz . For example, if a pure tone of 400 Hz is presented to the right ear and a pure tone of 410 Hz is [ 0079 ] If one considers neuron activity within the brain as presented simultaneously to the left ear, an amplitude modu a set of loosely coupled oscillators, then the various param lated standing wave of 10 Hz , the difference between the two eters that might be controlled include the size of the region tones , is experienced as the two wave forms mesh in and out of neurons , frequency of oscillation , resonant frequency or of phase within the superior olivary nuclei. This binaural time - constant, oscillator damping , noise , amplitude, cou beat is not heard in the ordinary sense of the word ( the pling to other oscillators , and of course , external influences human range of hearing is from 20-20,000 Hz ) . It is per that may include stimulation and / or power loss . In a human ceived as an auditory beat and theoretically can be used to brain , pharmacological intervention may be significant. For entrain specific neural rhythms through the frequency -fol example , drugs that alter excitability, such as caffeine , lowing response ( FFR )—the tendency for cortical potentials neurotransmitter release and reuptake, nerve conductance , to entrain to or resonate at the frequency of an external etc. can all influence operation of the neural oscillators . stimulus. Thus, it is theoretically possible to utilize a specific Likewise , sub - threshold external stimulation effects, includ binaural -beat frequency as a consciousness management ing DC , AC , and electromagnetic effects , can also influence technique to entrain a specific cortical rhythm . The binaural the operation of the neural oscillators . beat appears to be associated with an electroencephalo [ 0080 ] Phase resetting or shifting can synchronize inputs graphic ( EEG ) frequency - following response in the brain . and favor communication and , eventually, Hebbian plastic [ 0083 ] Uses of audio with embedded binaural beats that ity ( Hebb , 1949 ) . Thus, rhythmic stimulation may induce a are mixed with music or various pink or background sound statistically higher degree of coherence in spiking neurons , are diverse . They range from relaxation , meditation, stress which facilitates the induction of a specific cognitive process reduction , pain management, improved sleep quality, ( or hinders that process ) . Here , the perspective is slightly decrease in sleep requirements , super learning , enhanced different ( coherence resonance ), but the underlining mecha creativity and intuition, remote viewing , telepathy, and out nisms are similar to the ones described so far ( stochastic of- body experience and lucid dreaming . Audio embedded resonance ), and the additional key factor is the repetition at with binaural beats is often combined with various medita a specific rhythm of the stimulation . See , Cade “ The Awak tion techniques , as well as positive affirmations and visual ened Mind : Biofeedback and the Development of Higher ization . States of Awareness” ( Dell , 1979 ) ; Anna Wise, “ The High [ 0084 ] When signals of two different frequencies are pre Performance Mind : Mastering Brainwaves for Insight, Heal sented , one to each ear, the brain detects phase differences ing , and Creativity ” . between these signals . “ Under natural circumstances , a [ 0081 ] Entrainment is plausible because of the character detected phase difference would provide directional infor istics of the demonstrated EEG responses to a single TMS mation . The brain processes this anomalous information pulse , which have a spectral composition which resembles differently when these phase differences are heard with the spontaneous oscillations of the stimulated cortex . For stereo headphones or speakers. A perceptual integration of example , TMS of the “ resting ” visual ( Rosa nova et al . , the two signals takes place , producing the sensation of a 2009 ) or motor cortices ( Veniero et al . , 2011 ) triggers third “ beat ” frequency. The difference between the signals alpha - waves, the natural frequency at the resting state of waxes and wanes as the two different input frequencies mesh both types of cortices. With the entrainment hypothesis , the in and out of phase . As a result of these constantly increasing noise generation framework moves to a more complex and and decreasing differences, an amplitude -modulated stand extended level in which noise is synchronized with on - going ing wave the binaural beat is heard . The binaural beat is activity . Nevertheless, the model to explain the outcome will perceived as a fluctuating rhythm at the frequency of the not change, stimulation will interact with the system , and the difference between the two auditory inputs . Evidence sug final result will depend on introducing or modifying the gests that the binaural beats are generated in the brainstem's noise level . The entrainment hypothesis makes clear predic superior olivary nucleus, the first site of contralateral inte tions with respect to online repetitive TMS paradigms' gration in the auditory system . Studies also suggest that the frequency engagement as well as the possibility of inducing frequency - following response originates from the inferior phase alignment, i.e. , a reset of ongoing brain oscillations colliculus . This activity is conducted to the cortex where it via external spTIVS ( Thut et al . , 2011a , 2012 ; Veniero et al . , can be recorded by scalp electrodes . Binaural beats can 2011 ) . The entrainment hypothesis is superior to the local easily be heard at the low frequencies ( < 30 Hz ) that are ization approach in gaining knowledge about how the brain characteristic of the EEG spectrum . works, rather than where or when a single process occurs . [ 0085 ] Synchronized brainwaves have long been associ TMS pulses may phase - align the natural, ongoing oscillation ated with meditative and hypnogogic states , and audio with of the target cortex . When additional TMS pulses are deliv embedded binaural beats has the ability to induce and ered in synchrony with the phase - aligned oscillation ( i.e. , at improve such states of consciousness . The reason for this is the same frequency ), further synchronized phase - alignment physiological. Each ear is “ hardwired ” ( so to speak ) to both will occur , which will bring the oscillation of the target area hemispheres of the brain . Each hemisphere has its own in resonance with the TMS train . Thus, entrainment may be olivary nucleus (sound - processing center) which receives US 2020/0368491 A1 Nov. 26 , 2020 13 signals from each ear. In keeping with this physiological are n observations with p variables, then the number of structure, when a binaural beat is perceived there are actu distinct principal components is min ( n - 1 , p ) . This transfor ally two standing waves of equal amplitude and frequency mation is defined in such a way that the first principal present, one in each hemisphere. So , there are two separate component has the largest possible variance ( that is , standing waves entraining portions of each hemisphere to accounts for as much of the variability in the data as the same frequency. The binaural beats appear to contribute possible ) , and each succeeding component in turn has the to the hemispheric synchronization evidenced in meditative highest variance possible under the constraint that it is and hypnogogic states of consciousness . Brain function is orthogonal to the preceding components . The resulting vec also enhanced through the increase of cross - colossal com tors are an uncorrelated orthogonal basis set . This is useful munication between the left and right hemispheres of the for segregating components of a signal into self - correlated brain . See Reference List Table 9 . groups, and to segregate uncorrelated groups . PCA is sen [ 0086 ] Isochronic Tones : Isochronic tones are regular sitive to the relative scaling of the original variables. PCA is beats of a single tone that are used alongside monaural beats the simplest of the true eigenvector -based multivariate and binaural beats in the process called brainwave entrain analyses . Often , its operation can be thought of as revealing ment. At its simplest level , an isochronic tone is a tone that the internal structure of the data in a way that best explains is being turned on and off rapidly. They create sharp , the variance in the data . If a multivariate dataset is visualized distinctive pulses of sound . See Reference List Table 10 . as a set of coordinates in high - dimensional data space ( 1 axis [ 0087 ] Time- Frequency Analysis : Brian J. Roach and per variable ), PCA can supply the user with a lower Daniel H. Mathalon, “ Event -related EEG time- frequency dimensional picture, a projection of this object when viewed analysis: an overview of measures and analysis of early from its most informative viewpoint. This is done by using gamma band phase locking in schizophrenia. Schizophrenia only the first few principal components so that the dimen Bull . USA . 2008 ; 34 : 5 : 907-926 . , describes a mechanism for sionality of the transformed data is reduced . PCA is closely EEG time - frequency analysis . Fourier and wavelet trans related to factor analysis. Factor analysis typically incorpo forms ( and their inverse ) may be performed on EEG signals . rates more domain specific assumptions about the underly See Reference List Table 11 . ing structure and solves eigenvectors of a slightly different [ 0088 ] There are many approaches to time- frequency matrix . PCA is also related to canonical correlation analysis decomposition of EEG data , including the short - term Fou ( CCA ) . CCA defines coordinate systems that optimally rier transform ( STFT ) , ( Gabor D. Theory of Communica describe the cross - covariance between two datasets while tion . J. Inst . Electr. Engrs.1946 ; 93 : 429-457 ) continuous PCA defines a new orthogonal coordinate system that opti ( Daubechies I. Ten Lectures on Wavelets . Philadelphia , Pa .: mally describes variance in a single dataset. See , en.wiki Society for Industrial and Applied Mathematics; 1992 : 357 . pedia.org/wiki/Principal component analysis. 21. Combes J M , Grossmann A , Tchamitchian P. Wavelets : [ 0092 ] A general model for confirmatory factor analysis is Time -Frequency Methods and Phase Space - Proceedings of expressed as xFa + A & + € . The covariance matrix is expressed the International Conference ; Dec. 14-18 , 1987 ; Marseille , as E [ ( x - u ) ( x - u )' ] = AOA ' + O . If residual covariance matrix France ) or discrete ( Ma Ilat 5G . A theory for multi resolution = 0 and correlation matrix among latent factors O = I , then signal decomposition : the wavelet representation . IEEE factor analysis is equivalent to PCA and the resulting Trans Pattern Anal Mach Intel1.1989; 11 : 674-693 ) wavelet covariance matrix is simplified to E = AA '. When there are p transforms, Hilbert transform (Lyons R G. Understanding number of variables and all p components ( or factors ) are Digital Signal Processing. 2nd ed . Upper Saddle River, N.J .: extracted , this covariance matrix can alternatively be Prentice Hall PTR ; 2004 : 688 ) , and matching pursuits (Mal expressed into E = DAD ' , or > = DAD ' , where D = nxp lat S , Zhang Z. Matching pursuits with time - frequency orthogonal matrix of eigenvectors, and A = 2A , pxp matrix of dictionaries. IEEE Trans. Signal Proc 1993 ; 41 ( 12 )3397 eigenvalues, where à is a scalar, and A is a diagonal matrix 3415 ) . Prototype analysis systems may be implemented whose elements are proportional to the eigenvalues of E. The using, for example , MatLab with the Wavelet Toolbox , following three components determine the geometric fea www.mathworks.com/products/wavelet html. See Refer tures of the observed data : parameterizes the volume , D ence List Table 12 . indicates the orientation , and A represents the shape of the [ 0089 ] Single instruction , multiple data processors , such observation . as graphics processing units including the nVidia CUDA [ 0093 ] When population heterogeneity is explicitly environment or AMD Firepro high - performance computing hypothesized as in model - based cluster analysis, the environment are known, and may be employed for general observed covariance matrix is decomposed into the follow purpose computing , finding particular application in data ing general form = DAD " , where parameterizes the matrix transformations. See Reference List Table 13 . volume of the kth duster, D , indicates the orientation of that [ 0090 ] Statistical analysis may be presented in a form that duster, and Ak represents the shape of that duster. The permits parallelization, which can be efficiently imple subscript k indicates that each component ( or duster ) can mented using various parallel processors , a common form of have different volume , shape , and orientation . Assume a which is a SIMD (single instruction , multiple data ) proces random vector X , taking values R " , has a mean and sor , found in typical graphics processors ( GPUs ) . Artificial covariance matrix of uy and Ex, respectively . 1z > 2 > neural networks have been employed to analyze EEG sig > am > 0 are ordered eigenvalues of Ex such that the ith nals . See Reference List Table 14 . eigenvalue of Ex means the ith largest of them . Similarly, a [ 0091 ] Principal Component Analysis: Principal compo vector a , is the ith eigenvector of Ex when it corresponds to nent analysis ( PCA ) is a statistical procedure that uses an the eigenvalue of Ex. To derive the form of principal orthogonal transformation to convert a set of observations of components ( PCs ) , consider the optimization problem of possibly correlated variables into a set of values of linearly maximizing var?a , " X ] = a , " Ej , subject to a 0 , -1. The uncorrelated variables called principal components . If there Lagrange multiplier method is used to solve this question . US 2020/0368491 A1 Nov. 26 , 2020 14

L ( Q1 , 01 ) = a1 xQ1 + $ 1 ( 0 ] @ 1 - 1 ) , trace = al m dai A ;trace (cc ) = 2 x 01261Q1 = 0 = { xQp == $ 1 & 1 = var [c ? ¥ ] == $ 1a? Q1 = -41. i = 1 A tracele? c ) = ŽAcc = i = 1 Echo

Because - , is the eigenvalue of Ex with Q , being the Because CT C = B7 PPT B = B B = I , so corresponding normalized eigenvector, var?a , " X ] is maxi mized by choosing a to be the first eigenvector of Ex . In this case , zi = a , ” X is named the first PC of X , a , is the trace( CTC) = X = p , vector of coefficients for z1 , and var( z 1 ) = 21 : i = 1 j= 1 [ 0094 ] To find the second PC , 22 - a2T " X , we need to maximize var [a27X ] = a2 " Exa2 subject to z2 being uncor and the columns of C are orthonormal. By the Gram - Schmidt method , C can expand to D , such that related with 71 Because cova , " X , az X ) = 0 = a ? x D has its columns as an orthonormal basis of rm and 02-0 = a , az - 0 , this problem is equivalently set as maxi contains C as its first p columns. D is square shape, thus mizing a2 " ExO2, subject to a " az = 0 , and azt az = 1 . We still being an orthogonal matrix and having its rows as another make use of the Lagrange multiplier method orthonormal basis of RM . One row of C is a part of one row of D , so ol L ( Q2 , 01 , 02 ) = a ? a ? + Pia?a2 + 02 ( a?az – 1 ) dar. cis1, i = 1 , ... , m . Q2 +0101 + 20202 est, 212ap +9191 +26243 = 0 = ct { 2 X 10.09 Q2 = -02 Considering the constraints 0 = $ 1 = 0) = X Q2 = – 2Q2 = a?

[ 0095 ] Because – P2 is the eigenvalue of Ex , with a2 being the corresponding normalized eigenvector, var [ az " X ] is ?c51, ISi = 1 j = 1 -- maximized by choosing ay to be the second eigenvector of Ex . In this case , z 2 - a2 " X is named the second PC of X , A2 and the objective is the vector of coefficients for Z2 , and var ( z 2 ) = n2. Con= a T tinuing in this way , it can be shown that the i - th PC i X is constructed by selecting Q ; to be the ith eigenvectorZ i of Ex , and has a variance of hz . The key result in regards to PCA is that the principal components are the only set of ??i = 1 ) linear functions of original data that are uncorrelated and have orthogonal vectors of coefficients . [ 0096 ] For any positive integer psm , let B = [ B1 , B2 , . We derive that trace ( y) is maximized if Bpl be an real mxp matrix with orthonormal columns, i.e. , BiTB , = dj , and Y = B X . Then the trace of covariance matrix of Y is maximized by taking B = [ Q1 , A2 , ... , ap ], where di is the i - th eigenvector of Ex. Because Ey is symmetric with 8-1 all distinct eigenvalues so { a1 , B2 , ... , Pm } is an ortho normal basis with a ; being the i -th eigenvector of Ex, and we can represent the columns of B as for i = 1 , ... , p , and

B : = < jQj, i = 1 , ... , p , c j = 1 P. ?? =0 for i = p + 1 ... , m . When B = faj, B2 , ... , a ], straightforward So we have B = PC , where P = [ a1 , . am ] , C = { c } is an calculation yields that C is an all zero matrix except ci = 1 , mxp matrix . Then , PT Px P = A , with A being a diagonal i = 1 , ... , p . This fulfills the maximization condition . matrix whose k - th diagonal element is kg and the covariance Actually, by taking B = [ Y? , Y2 , Yol, where { Y1 , Yz , Yp } is any matrix of Y is , orthonormal basis of the subspace of span { Q1 , A2 , ... , ap }, [ 0097 ] Ey = B ]' Ex B =CTPT Ex PC = CT AC = 1 , C , C ++ ... the maximization condition is also satisfied , yielding the + àm Cm Cm7, where c ;? is the i - th row of C. So , same trace of the covariance matrix of Y. US 2020/0368491 A1 Nov. 26 , 2020 15

[ 0098 ] Suppose that we wish to approximate the random nation for each X4 such that zk = X & Vk . Let Ck be the matrix vector X by its projection onto a subspace spanned by of dimension Pkxm (mspk ), associated to the external infor columns of B , where B = [ B1 , Y2 , 9 Bm ] , is a real mxp mation explanatory variables of set k . matrix with orthonormal columns, i.e. , B : 1 B ; = 8, ;. If o , is the [ 0105 ] Generalized CPCA ( GCPCA ) ( Amenta , D'Ambra , residual variance for each component of X , then 1999 ) with external information consists in seeking for K coefficients vectors Vk ( or, in same way , K linear combina tions zk ) subject to the restriction Ck'V = 0 simultaneously, such that: ??i = 1 ?

K K ( 1 ) is minimized if B = [ Q1 , A2 , . ap ], where { Q1, A2, max {y'X ; vi , Y'XjV ;) Bp } are the first p eigenvectors of Ex. In other words, the i = 1 j = 1 trace of the covariance matrix of X - BBT X is minimized if B = a , C2 , ... , ap ). When E ( X ) = 0 , which is a commonly ||Xx vk lll2 = 1 k = 1 applied preprocessing step in data analysis methods, this with the constraints property is saying that E || X - BB7X || is minimized if B = fa , K Chvk = 0 Az apl. The projection of a random vector X onto a k = 1 subspace spanned by columns of B is ÎN = BBT X. Then the residual vector is € = X - BB7 X , which has a covariance matrix X = ( 1 - BB7) Ex { I - BB ). Then, or , in equivalent way ,

maxv ' ( A'A ) v maxf'B - 0.5 A'AB - 0.5f + BB? BB " V'Bv = 1 or f ' f = 1 Žv - unser - -{ X - <« »--- <<< with the constraints with the constraints - C'v = 0 C'v = 0 [ 0099 ] Also , we know : [ 0100 ] trace (Ex BB +) trace (BBT Extrace ( B + Ey B ) [ 0101 ] trace (BBTE , BBT) trace( B ? Ex BBT B ) = trace (BT [ 0106 ] where A = Y'X , B = diag ( X ' X , ... , XxXx ), C = C ' ExB ) ... IC5' ) , v ' = ( v ," 1 ... IV , ') and f = B0-5vwith [ 0102 ] The last equation comes from the fact that B has orthonormal columns . So , | X / YYX1 X / YY'XK A'A = : [ XRYY XI XKYYXk

???i = 1 = trace ( 2x ) - trace[ B" / B ). [ 0107 ] The constrained maximum problem turns out to be To minimize an extension of criterion sup / Zulp = 12,8x Z ?o zk) (Sabatier , 1993 ) with more sets of criterion variables with external information . The solution of this constrained maximum problem leads to solve the eigen - equation ??? , (Px - PxB - 1c ) Wyg = ng, [0108 ] where g = Xv , Py - PXB ** c = & k = 1 [ text missing or it suffices to maximize trace ( BTEB ). This can be done by illegible when filed ] (Px :-P x ( x :x ) is the oblique pro jector operator associated to the direct süm decomposition of thechoosing first p Beigenvectors = [ Q? , Az , of@p ex ], , whereas above {Q? . , A2 , ... , Qp} are R., R " = Im ( P x -PXBJÓIm (PJÓKer (Px ) with Px = X [ 0103 ] See , Pietro Amenta , Luigi D'Ambra, “ Generalized ( X 'xx ) -- XX andPC (CB - C ) CB - , respectively, 7 and Constrained Principal Component Analysis with External B - T orthogonal projector operators onto the subspaces Information , ” ( 2000 ) . We assume that data on K sets of spannedPx ' = XB by -the C (columnsCB - C of) - 'matrices C' B - 1 X X ' is andthe C.orthogonal Furthermore pro, explanatory variables and S criterion variables of n statisti jector operator onto the subspace spanned the columns of the cal units are collected in matrices XK ( k = 1 , ... K ) and Y , matrix XB - C . Starting from the relation ( s = 1 , ... , S ) of orders (nxp? ) , ... , ( nxpx ) and (nxq? ) , . . , (nxqs ), respectively . We suppose , without loss of gener (P x -xxxx- ) Wyg =MVE ality, identity matrices for the metrics of the spaces of (which is obtained from the expression ( I - P ) X'W / 8 = 2Bv) variables of X and Y , with Dr = diag ( 1 / n ), weight matrix of the coefficients vectors Vk and the linear combinations statistical units . We assume , moreover , that Xi's and Y.'s are Zk = X V maximizing ( 1 ) can be given by the relations centered as to the weights Dn . [ 0104 ] Let X = [ X | | Xk ] and Y = [ Y ! | Y) , respectively, be K and S matrices column linked to orders ( n = ExPx ) and ( nx , q ) . Let be , also , W - YY ' while we = (( Xx { Xx ) = f (1 – Pcx ] X { WyXv and denote vk the coefficients vector ( Pk , 1 ) of the linear combi US 2020/0368491 Al Nov. 26 , 2020 16

sition of a covariance matrix , and not directly to a frequency -continued domain decomposition . ( see en.wikipeda.org/wiki/Singular_ z = ( Px – Pxxvxxs= )WyXV . spectrum_analysis .) [ 0118 ] In practice , SSA is a nonparametric spectral esti respectively . mation method based on embedding a time series { X (t ): t = 1 , ( 0109 ] The solution eigenvector g can be written , as the ... N } in a vector space of dimension M. SSA proceeds by sum of the linear combinations z kig = % XVk. Notice that the diagonalizing the MxM lag - covariance matrix Crof X (t ) to eigenvalues associated to the eigen - system are , according to obtain spectral information on the time series, assumed to be the Sturm theorem , lower or equal than those of GCPCA stationary in the weak sense . The matrix Cy can be estimated eigen system : Ek= 1 KPx, Wyg = ng , See Reference List Table directly from the data as a Toeplitz matrix with constant 15 . diagonals , i.e. , its entries C ; depend only on the lag li - jl: [ 0110 ] Spatial Principal Component Analysis [ 0111 ] Let J ( t , i ; a , s ) be the current density in voxel i , as 1 N - i - j| estimated by LORETA , in condition a at t time - frames after Cij = N - 11 - i X (t ) / (1+ |i – j1) stimulus onset for subject s . Let area : Voxel-> BA be a 1 = 1 function , which assigns to each voxel iEVoxel the corre sponding fBABEBA . In a first pre - processing step , we calculate for each subjects the value of the current density [ 0119 ] An alternative way to compute Cx, is by using the averaged over each Fba N'XM “ trajectory matrix ” D that is formed by M lag - shifted copies of X (t ), which are N ' = N - M + 1 long ; then

X ( 1 , b ; a ,s ) = (t , i , Q , S ) 1 ieb Cx = N- ' DD

[ 0112 ] where N , is the number of voxels in the f?Ab , in [ 0120 ] The M eigenvectors Ex of the lag -covariance condition a for subjects. matrix Cy are called temporal empirical orthogonal func [ 0113 ] In the second analysis stage , the mean current tions ( EOFs ) . The eigenvalues àze of Cy account for the density xít , b ; a , s ) from each f?Ab , for every subjects and partial variance in the direction Ek and the sum of the conditionoa , was subjected to spatial PCA analysis of the eigenvalues, i.e. , the trace of Ca gives the total variance of correlation matrix and varimax rotation the original time series X (t ). The name of the method derives [ 0114 ] In the present study, the spatial PCA uses the from the singular values ak !1/2 of Cx above -defined f?As as variables sampled along the time epoch for which EEG has been sampled ( 0-1000 ms ; 512 [ 0121 ] Projecting the time series onto each EOF yields the time- frames ), and the inverse solution was estimated . Spatial corresponding temporal principal components ( PCs) IV matrices ( each matrix was sized bxt = 36x512 elements) for every subject and condition were collected , and subjected to PCA analyses , including the calculation of the covariance Ax (t ) = X ( t + j- 1) Ex( j) . matrix ; eigenvalue decomposition and varimax rotation , in j = 1 order to maximize factor loadings . In other words , in the spatial PCA analysis, we approximate the mean current density for each subject in each condition as ( 2 ) [ 0122 ] An oscillatory mode is characterized by a pair of nearly equal SSA eigenvalues and associated PCs that are in approximate phase quadrature, which can efficiently repre x {t ; a , s ) < xola , s ) + Xcx( + ] xx (Q , s ) , sent a nonlinear, nonharmonic oscillation , due to the fact that k a single pair of data - adaptive SSA eigenmodes often will capture the basic periodicity of an oscillatory mode than [ 0115 ] where here xét ; a , s ) ER36 is a vector, which denotes fixed basis functions, such as the sines and cosines used in the time - dependent activation of the fBAs, x , ( a , s ) is their the Fourier transform . mean activation , and xza , s ) and C are the principal [ 0123 ] The window width M determines the longest peri components and their corresponding coefficients ( factor odicity captured by SSA . Signal -to - noise separation can be loadings ) as computed using the principal PCA . obtained by merely inspecting the slope break in a “ scree [ 0116 ] See , download . ]ww.com/wolterskluwer.com/ diagram " of eigenvalues or singular values 1/2 vs. k . The WNR_1_1_2010_03_22_ARZY_1_SDC1.doc . point k * = Sat which this break occurs should not be confused [ 0117 ] Singular spectrum analysis ( SSA ) : SSA is a non with a “ dimension ” Oaf the underlying deterministic dynam parametric spectral estimation method . It combines elements ics . of classical time series analysis, multivariate statistics , mul [ 0124 ] A Monte - Carlo test can be applied to ascertain the tivariate geometry, dynamical systems, and signal process statistical significance of the oscillatory pairs detected by ing . SSA can be an aid in the decomposition of time series SSA . The entire time series or parts of it that correspond to into a sum of components , each having a meaningful inter trends, oscillatory modes or noise can be reconstructed by pretation . The name “ singular spectrum analysis ” relates to using linear combinations of the PCs and EOFs , which the spectrum of eigenvalues in a singular value decompo provide the reconstructed components ( RCs ) RK: US 2020/0368491 Al Nov. 26 , 2020 17

problem -dependent . However, it is commonly measured by the intrinsic dimensionality and / or the smoothness of the Rx (t ) = M ŽA: { t – j+ 1) Ex ( ) ; manifold . Usually, the principal manifold is defined as a KEK ; = Lt solution to an optimization problem . The objective function includes quality of data approximation and some penalty [ 0125 ] here K is the set of EOFs on which the reconstruc terms for the bending of the manifold . The popular initial tion is based . The values of the normalization factor M , as approximations are generated by linear PCA , Kohonen's well as of the lower and upper bound of summation L , and SOM or autoencoders . The elastic map method provides the U ,, differ between the central part of the time series and the expectation -maximization algorithm for principal manifold vicinity of its endpoints. learning with minimization of quadratic energy functional at [ 0126 ] Mufti - channel SSA ( or M - SSA ) is a natural exten the " maximization ” step . sion of SSA to an L - channel time series of vectors or maps [ 0131 ] An autoencoder is a feed - forward neural network with N data points { X /( t ): 1 = 1 , ... , L ; t = 1 , . , N } . The which is trained to approximate the identity function . That extended EOF ( EEOF ) analysis is sometimes assumed to be is , it is trained to map from a vector of values to the same synonymous with M - SSA . The two methods are both exten vector . When used for dimensionality reduction purposes, sions of classical principal component analysis ( PCA ) but one of the hidden layers in the network is limited to contain they differ in emphasis: EEOF analysis typically utilizes a only a small number of network units . Thus , the network number Lot spatial channels much greater than the number must learn to encode the vector into a small number of M of temporal lags, thus limiting the temporal and spectral dimensions and then decode it back into the original space . information . In M - SSA , on the other hand , one usually Thus, the first half of the network is a model which maps chooses ( ten M - SSA is applied to a few leading PCs of the from high to low - dimensional space , and the second half spatial data, with M chosen large enough to extract detailed maps from low to high - dimensional space . Although the temporal and spectral information from the multivariate time idea of autoencoders is quite old , training of deep autoen series. To avoid a loss of spectral properties, VARIMAX coders has only recently become possible through the use of rotation of the spatio - temporal EOFs ( ST -EOFs ) of the restricted Boltzmann machines and stacked denoising auto M - SSA and its variations are sometimes used . Alternatively, encoders. Related to autoencoders is the NeuroScale algo a closed matrix formulation of the algorithm for the simul rithm , which uses stress functions inspired by multidimen taneous rotation of the EOFs by iterative SVD decomposi sional scaling and Sammon mappings ( see below ) to learn a tions has been proposed . non - linear mapping from the high - dimensional to the [ 0127 ] Nonlinear Dimensionality Reduction : embedded space . The mappings in NeuroScale are based on [ 0128 ] High - dimensional data , meaning data that requires radial basis function networks . more than two or three dimensions to represent, can be [ 0132 ] Gaussian process latent variable models (GPLVM ) difficult to interpret. One approach to simplification is to are probabilistic dimensionality reduction methods that use assume that the data of interest lie on an embedded non Gaussian Processes ( GPs ) to find a lower dimensional linear manifold within the higher -dimensional space . If the non - linear embedding of high dimensional data . They manifold is of low enough dimension , the data can be extend the Probabilistic formulation of PCA . The model is visualized in the low - dimensional space . Non - linear meth defined probabilistically, and the latent variables are then ods can be broadly classified into two groups: those that marginalized , and parameters are obtained by maximizing provide a mapping ( either from the high - dimensional space the likelihood . Like kernel PCA , they use a kernel function to the low - dimensional embedding or vice versa ), and those to form a nonlinear mapping ( in the form of a Gaussian that just give a visualization . In the context of machine process ). However , the GPLVM maps from the embedded learning, mapping methods may be viewed as a preliminary ( latent) space to the data space ( like density networks and feature extraction step , after which pattern recognition algo GTM ) whereas in kernel PCA is opposite . It permits visu rithms are applied . Typically, those that just give a visual alization of high dimensional data and construction of a ization are based on proximity data - that is , distance mea shared manifold model between two observation spaces . surements . Related Linear Decomposition Methods include GPLVM and its many variants have been proposed specially Independent component analysis ( ICA ) , Principal compo for human motion modeling, e.g. , back constrained GPLVM , nent analysis ( PCA ) ( also called Karhunen -Loève trans GP dynamic model ( GPDM ) , balanced GPDM ( B - GPDM ) form - KLT ), Singular value decomposition ( SVD ) , and and topologically constrained GPDM . To capture the cou Factor analysis. pling effect of the pose and gait manifolds in the gait [ 0129 ] The self -organizing map ( SOM , also called analysis, a multi -layer joint gait - pose manifold was pro Kohonen map ) and its probabilistic variant generative topo posed . graphic mapping ( GTM ) use a point representation in the [ 0133 ] Curvilinear component analysis ( CCA ) looks for embedded space to form a latent variable model based on a the configuration of points in the output space that preserves non - linear mapping from the embedded space to the high original distances as much as possible while focusing on dimensional space . These techniques are related to work on small distances in the output space ( conversely to Sammon's density networks, which also are based around the same mapping which focuses on small distances in original probabilistic model . space ) . It should be noticed that CCA , as an iterative [ 0130 ] Principal curves and manifolds give the natural learning algorithm , actually starts with a focus on large geometric framework for nonlinear dimensionality reduc distances ( like the Sammon algorithm ), then gradually tion and extend the geometric interpretation of PCA by change focus to small distances. The small distance infor explicitly constructing an embedded manifold , and by mation will overwrite the large distance information if encoding using standard geometric projection onto the mani compromises between the two have to be made . The stress fold . How to define the “ simplicity ” of the manifold is function of CCA is related to a sum of right Bregman US 2020/0368491 A1 Nov. 26 , 2020 18 divergences. Curvilinear distance analysis ( CDA ) trains a factors termed content' and ' style ' , where 'content is the self - organizing neural network to fit the manifold and seeks invariant factor related to the essence of the class and ‘ style ' to preserve geodesic distances in its embedding. It is based expresses variations in that class between instances . Unfor on Curvilinear Component Analysis ( which extended Sam tunately , Laplacian Eigenmaps may fail to produce a coher mon's mapping ), but uses geodesic distances instead . Dif ent representation of a class of interest when training data feomorphic Dimensionality Reduction or Diffeomap learns consist of instances varying significantly in terms of style . In a smooth diffeomorphic mapping which transports the data the case of classes which are represented by multivariate onto a lower -dimensional linear subspace . The method sequences , Structural Laplacian Eigenmaps has been pro solves for a smooth time indexed vector field such that flows posed to overcome this issue by adding additional con along the field which starts at the data points will end at a straints within the Laplacian Eigenmaps neighborhood lower - dimensional linear subspace, thereby attempting to information graph to better reflect the intrinsic structure of preserve pairwise differences under both the forward and the class . More specifically , the graph is used to encode both inverse mapping the sequential structure of the multivariate sequences and , to [ 0134 ] Perhaps the most widely used algorithm for mani minimize stylistic variations, the proximity between data fold learning is Kernel principal component analysis ( kernel points of different sequences or even within a sequence, if it PCA ) . It is a combination of PCA and the kernel trick PCA contains repetitions. Using dynamic time warping , proxim begins by computing the covariance matrix of the Mxn ity is detected by finding correspondences between and Matrix X. It then projects the data onto the first k eigenvec within sections of the multivariate sequences that exhibit tors of that matrix . KPCA begins by computing the covari high similarity. Like LLE , Hessian LLE is also based on sparse matrix techniques. It tends to yield results of a much ance matrix of the data after being transformed into a higher quality than LLE . Unfortunately, it has a very costly higher -dimensional space . It then projects the transformed computational complexity, so it is not well - suited for heavily data onto the first k eigenvectors of that matrix , just like sampled manifolds. It has no internal model . Modified LLE PCA . It uses the kernel trick to factor away much of the ( MLLE ) is another LLE variant which uses multiple weights computation , such that the entire process can be performed in each neighborhood to address the local weight matrix without actually computing O ( x ) . Of course , O must be conditioning problem which leads to distortions in LLE chosen such that it has a known corresponding kernel. maps . MLLE produces robust projections similar to Hessian [ 0135 ] Laplacian Eigenmaps, ( also known as Local Linear LLE , but without the significant additional computational Eigenmaps, LLE ) are special cases of kernel PCA , per cost . formed by constructing a data -dependent kernel matrix . ( 0136 ] Manifold alignment takes advantage of the KPCA has an internal so it can be used to map points assumption that disparate data sets produced by similar ontoLaplacian its embedding Eigenmaps that uses were spectral not available techniques at training to perform time . generating processes will share a similar underlying mani dimensionality reduction . This technique relies on the basic fold representation . By learning projections from each origi assumption that the data lies in a low - dimensional manifold nal space to the shared manifold , correspondences are recov in a high - dimensional space . This algorithm cannot embed ered and knowledge from one domain can be transferred to out of sample points , but techniques based on Reproducing another. Most manifold alignment techniques consider only kernel Hilbert space regularization exist for adding this two data sets , but the concept extends to arbitrarily many capability. Such techniques can be applied to other nonlinear initial data sets . Diffusion maps leverage the relationship dimensionality reduction algorithms as well . Traditional between heat diffusion and a random walk (Markov Chain ); techniques like principal component analysis do not con an analogy is drawn between the diffusion operator on a sider the intrinsic geometry of the data . Laplacian eigen manifold and a Markov transition matrix operating on maps builds a graph from neighborhood information of the functions defined on the graph whose nodes were sampled data set . Each data point serves as a node on the graph and from the manifold . The relational perspective map is a connectivity between nodes is governed by the proximity of multidimensional scaling algorithm . The algorithm finds a neighboring points ( using e.g. the k -nearest neighbor algo configuration of data points on a manifold by simulating a rithm ). The graph thus generated can be considered as a mufti- particle dynamic system on a closed manifold , where discrete approximation of the low - dimensional manifold in data points are mapped to particles and distances ( or dis the high - dimensional space . Minimization of a cost function similarity ) between data points represent a repulsive force. based on the graph ensures that points close to each other on As the manifold gradually grows in size , the multi- particle the manifold are mapped close to each other in the low system cools down gradually and converges to a configu dimensional space , preserving local distances . The eigen ration that reflects the distance information of the data functions of the Laplace -Beltrami operator on the manifold points. Local tangent space alignment ( LISA ) is based on the serve as the embedding dimensions, since under mild con intuition that when a manifold is correctly unfolded , all of ditions this operator has a countable spectrum that is a basis the tangent hyperplanes to the manifold will become for square integrable functions on the manifold ( compare to aligned . It begins by computing the k - nearest neighbors of Fourier series on the unit circle manifold ). Attempts to place every point. It computes the tangent space at every point by Laplacian eigenmaps on solid theoretical ground have met computing the d - first principal components in each local with some success , as under certain nonrestrictive assump neighborhood. It then optimizes to find an embedding that tions , the graph Laplacian matrix has been shown to con aligns the tangent spaces . Local Multidimensional Scaling verge to the Laplace -Beltrami operator as the number of performs multidimensional scaling in local regions and then points goes to infinity. In classification applications, low uses convex optimization to fit all the pieces together. dimension manifolds can be used to model data classes [ 0137 ] Maximum Variance Unfolding was formerly which can be defined from sets of observed instances . Each known as Semidefinite Embedding. The intuition for this observed instance can be described by two independent algorithm is that when a manifold is properly unfolded , the US 2020/0368491 A1 Nov. 26 , 2020 19 variance over the points is maximized . This algorithm also duced event- related coherence ( ERCOH ) . A wide variety of begins by finding the k - nearest neighbors of every point. It other signal processing measures have been tested for use on then seeks to solve the problem of maximizing the distance EEG and / or MEG data , including dimensionality measures between all non -neighboring points, constrained such that based on chaos theory and the bispectrum . Use of neural the distances between neighboring points are preserved . networks has also been proposed for EEG pattern recogni Nonlinear PCA (NLPCA ) uses backpropagation to train a tion applied to clinical and practical problems , though multi - layer perception ( MLP ) to fit to a manifold . Unlike usually these methods have not been employed with the aim typical MLP training, which only updates the weights, of explicitly modeling the neurodynamics involved . Neuro NLPCA updates both the weights and the inputs. That is , dynamics is the mobilization of the nervous system as an both the weights and inputs are treated as latent values . After approach to physical treatment . The method relies on influ training, the latent inputs are a low - dimensional represen encing pain and other neural physiology via the mechanical tation of the observed vectors , and the MLP maps from that treatment of neural tissues and the non -neural structures low -dimensional representation to the high - dimensional surrounding the nervous system . The body presents the observation space . Manifold Sculpting uses graduated opti nervous system with a mechanical interface via the muscu mization to find an embedding. Like other algorithms, it loskeletal system . With movement, the musculoskeletal sys computes the k - nea rest neighbors and tries to seek an tem exerts non - uniform stresses and movement in neural embedding that preserves relationships in local neighbor tissues , depending on the local anatomical and mechanical hoods . It slowly scales variance out of higher dimensions, characteristics and the pattern of body movement . This while simultaneously adjusting points in lower dimensions activates an array of mechanical and physiological responses to preserve those relationships. in neural tissues . These responses include neural sliding , [ 0138 ] Ruffini ( 2015 ) discusses Multichannel transcranial pressurization , elongation , tension , and changes in intran current stimulation ( tCS ) systems that offer the possibility of eural microcirculation , axonal transport, and impulse traffic . EEG - guided optimized , non - invasive brain stimulation . A [ 0140 ] The availability of and interest in larger and larger tCS electric field realistic brain model is used to create a numbers of EEG ( and MEG ) channels led immediately to forward “ lead - field ” matrix and , from that, an EEG inverter the question ofhow to combine data from different channels . is employed for cortical mapping. Starting from EEG , 2D Donchin advocated the use of linear factor analysis methods cortical surface dipole fields are defined that could produce based on principal component analysis ( PCA ) for this pur the observed EEG electrode voltages . Schestatsky et al. pose . Temporal PCA assumes that the time course of acti ( 2017 ) discuss transcranial direct current stimulation vation of each derived component is the same in all data ( tDCS ) , which stimulates through the scalp with a constant conditions . Because this is unreasonable for many data sets , electric current that induces shifts in neuronal membrane spatial PCA ( usually followed by a component rotation excitability , resulting in secondary changes in cortical activ procedure such as Varimax or Promax ) is of potentially ity . Although tDCS has most of its neuromodulatory effects greater interest . To this end , several variants of PCA have on the underlying cortex , DCS effects can also be observed been proposed for ERP decomposition . in distant neural networks . Concomitant EEG monitoring of [ 0141 ] Bell and Sejnowski published an iterative algo the effects of tDCS can provide valuable information on the rithm based on information theory for decomposing linearly mechanisms of tDCS . EEG findings can be an important mixed signals into temporally independent by minimizing surrogate marker for the effects of tDCS and thus can be their mutual information . First approaches to blind source used to optimize its parameters. This combined EEG - DCS separation minimized third and fourth - order correlations system can also be used for preventive treatment of neuro among the observed variables and achieved limited success logical conditions characterized by abnormal peaks of cor in simulations. A generalized approach uses a simple neural tical excitability, such as seizures . Such a system would be network algorithm that used joint information maximization the basis of a non - invasive closed -loop device. tDCS and or ‘ infomax ’ as a training criterion . By using a compressive EEG can be used concurrently . See Reference List Table 16 . nonlinearity to transform the data and then following the [ 0139 ] EEG analysis approaches have emerged , in which entropy gradient of the resulting mixtures, ten recorded event - related changes in EEG dynamics in single event voice and music sound sources were unmixed . A similar related data records are analyzed . See Allen D. Malony et al . , approach was used for performing blind deconvolution , and Computational Neuroinformatics for Integrated Electromag the ‘ infomax ’ method was used for decomposition of visual netic Neuroimaging and Analysis, PAR - 99-138 . scenes . Pfurtscheller, reported a method for quantifying the average [ 0142 ] Blind decomposition of time series applies to the transient suppression of alpha band ( circa 10 - Hz ) activity infomax independent component analysis ( ICA ) for decom following stimulation . Event - related desynchronization position of EEG and event- related potential ( ERP ) data and ( ERD , spectral amplitude decreases ), and event - related syn reported the use of ICA to monitor alertness . This separated chronization ( ERS , spectral amplitude increases ) are artifacts and EEG data into constituent components defined observed in a variety of narrow frequency bands ( 4-40 Hz ) by spatial stability and temporal independence. ICA can also which are systematically dependent on task and cognitive be used to remove artifacts from continuous or event - related state variables as well as on stimulus parameters. Ma keig ( single -trial ) EEG data prior to averaging. Viga no et al . ( 1993 ) was reported event - related changes in the full EEG ( 1997 ) . spectrum , yielding a 2 - D time / frequency measure he called [ 0143 ] Since the publication of the original infomax ICA the event -related spectral perturbation ( ERSP ) . This method algorithm , several extensions have been proposed . Incorpo avoided problems associated with the analysis of a priori ration of a ' natural gradient term avoided matrix inversions , narrow frequency bands, since bands of interest for the greatly speeding the convergence of the algorithm and analysis could be based on significant features of the com making it practical for use with personal computers on large plete time / frequency transform . Rappelsburger et al . intro data EEG and fMRI data sets . An initial ‘ sphering' step US 2020/0368491 A1 Nov. 26 , 2020 20 further increased the reliability of convergence of the algo auditory or visual stimulation preferably is modulated to rithm . The original algorithm assumed that sources have synchronize or control brainwave patterns, in addition to any sparse ' ( super -Gaussian ) distributions of activation values . overt sensory effects that may be provided . The measure This restriction has recently been relaxed in an “ extended ment of brain activity, and brain stimulation may be accord ICA ’ algorithm that allows both super - Gaussian and sub ing to the known methods described hereinabove, without Gaussian sources to be identified . A number of variant ICA limitation, though the preferred implementation is such that algorithms have appeared in the signal processing literature . the subject need not be encumbered by bulky, uncomfort In general, these make more specific assumptions about the able , expensive, or exotic equipment, and can be used temporal or spatial structure of the components to be sepa unmonitored in a home environment. However, the inven rated and typically are more computationally intensive than tion is not limited by such constraints , so long as the sleep the infomax algorithm . pattern is effectively controlled . [ 0144 ] Since individual electrodes ( or magnetic sensors ) [ 0148 ] In some cases , the invention is used for other than each record a mixture of brain and non -brain sources , sleep induction , and may also be used to control other stages spectral measures are difficult to interpret and compare of consciousness or other mental or emotional states , and across scalp channels . For example, an increase in coherence preferably a desired sequence of states based on subject between two electrode signals may reflect the activation of biology a strong brain source projecting to both electrodes or the [ 0149 ] Brain - states , which correlate with specific cogni deactivation of a brain generator projecting mainly to one of tive states , may be monitored with non - invasive techniques the electrodes. If independent components of the EEG ( or such as EEG and MEG that indirectly measure cortical MEG ) data can be considered to measure activity within activity. These cortical signatures provide insight into the functionally distinct brain networks, however, event -related neuronal activity, which has been used to identify abnormal coherence between independent components may reveal cortical function in numerous neurological and psychiatric transient, event - related changes in their coupling and decou conditions . Further, the induction of entrained cortical pling ( at one or more EEG /MEG frequencies ). ERCOH rhythms via transcranial stimulation is effective in imbuing analysis has been applied to independent EEG components brain - states correlated with such cortical rhythms. See , in a selective attention task . Poltorak , Alexander . 2019. “ On the Possibility of Trans [ 0145 ] Because sleep patterns have characteristic eye planting Mental States . ” OSF Preprints. April 16. doi : 10 . movements , e.g. , rapid eye movements ( REM ) , EOG may 31219 / osf.io /sjqfx . provide in indication of sleep state . Electromyography : [ 0150 ] It has been suggested by Crick and Koch [ Crick F , Electromyography ( EMG ) evaluates and records electrical Koch C Towards a neurobiological theory of consciousness . activity produced by skeletal muscles. EMG reflects muscle Seminars in the Neurosciences, 1990 ; ( 2 ) , 263-75 . ] ( see also tone , which can be indicative of a sleep state . See , Reference [ Rees G , Kreiman G , Koch C Neural correlates of con List Table 17 . sciousness in humans . Nature Reviews . Neuroscience 2002 ; 3 ( 4 ) 261-270 . doi.org/10.1038/nrn783 ]) that every mental SUMMARY OF THE INVENTION state is expressed through unique neural signals, such as [ 0146 ] The present invention provides a system and frequency oscillations , that are correlated with mood , cog method for inducing brain states in a subject human or nition and motor functions. It is thus possible to induce a animal which represent or are conducive to sleep according desired mental state by replicating its neural correlates . to a natural sleep cycle , based on brain activity patterns ( e.g. , [ 0151 ] Functional neuroimaging, such as electroencepha brainwaves) a representative the same species , or the lography ( EEG ) or magnetoencephalography ( MEG ) , can same individual, which are processed and converted to a capture the neuronal activity of localized brain regions stimulatory signal that is then imposed on the subject. At any which correlate with distinct cognitive or behavioral states time , the stimulation of the subject seeks to induce EEG (mental states ). EEG recordings have demonstrated , for patterns that are representative of a target sleep state . The example, that the pattern of brain activity changes during sequence of sleep states may be from the species or indi meditative acts , and frontal cortex EEG activity has been vidual , or designed according to an algorithm designed or associated with emotion induction and regulation Nu et al . optimized to achieve the overall sleep pattern comprising the [ Yu X Fumoto M , Nakatani Y , Sekiya ma T , Kikuchi H , Seki desired sequence of sleep states . Advantageously , the subject Y , Sato - Suzuki I , Arita H. Activation of the anterior pre is monitored during stimulation to measure response to the frontal cortex and serotonergic system is associated with stimulation , and / or interruption of the target sleep cycle . In improvements in mood and EEG changes induced by Zen case of interruption of the sleep cycle , the stimulation is meditation practice in novices . Intl of Psychophysiology restarted based on the state of the subject and , therefore , 2011 ; 80 ( 2 ), 103-111.doi.org / 10.1016 / j.ijpsycho.2011.02 . progresses again through a natural sequence of sleep states . 004 ) ; Dennis and Solomon [ Dennis TA , Solomon B. Frontal [ 0147 ] In some cases , the goal is to achieve a sleep cycle EEG and emotion regulation : electrocortical activity in which differs from a normal or natural sleep cycle and may response to emotional film dips is associated with reduced be modified or controlled in the case of organic pathology or mood induction and attention interference effects . Biological mental illness . The preferred stimulation is non - invasive , Psychology 2010 ; 85 ( 3 ) , 456-464 . doi.org/10.1016/j.biopsy and also preferably not electrical. However, transcranial cho.2010.09.008 ] ). EEG recordings reflect ionic fluctuations magnetic stimulation , e.g. , subthreshold PEMF, may be resultant of neuronal communication in the cortex arising employed , alone or with auditory, visual , or other stimula from dendritic depolarizations (Nunez and Srinivasan tion . For example, while visual stimulation may be contrain [Nunez P L , Srinivasan R. Electric fields of the brain : the dicated for entry into respectively deeper sleep states , it may neurophysics of EEG ( 2 ed ). Oxford : Oxford Univ . Press ; be advantageously used for moving the subject to a shal 2006. ] ) . Alternatively, MEG measurements reflect intracel lower sleep state , and to awaken the subject. However, such lular ionic fluctuations, which similarly result from action US 2020/0368491 A1 Nov. 26 , 2020 21 potentials ( Hämäläinen et al . [ Hämäläinen M , Han R , Ilmo including sleep , attention , and learning, as well as emotional niemi RJ , Knuutila J , Lounasmaa O V. Magnetoencepha valence . See FIG . 28. Attention - states in the brain are lography — theory, instrumentation , and applications to non primarily the result of the cognitive process of suppressing invasive studies of the working human brain . Rev. Mod . the detection of erroneous stimuli. This cognitive state is Phys. 1993 ; 65 , 413. doi.org/10.1103/revmodphys.65.413 ] ). associated with specific neuronal oscillations ( Schroeder et In both cases , the output measures correlate with localized al . [ Schroeder CE , Wilson D A Radman T , Scharf man H , cortical activity. Lakatos P. Dynamics of Active Sensing and perceptual [ 0152 ] These EEG or MEG signatures may be inverted in selection . Current Opinion in Neurobiology 2010 ; 20 ( 2 ) , order to stimulate, rather than record , cortical activity . 172-176 . doi.org/10.1016/j.conb.2010.02.010 ]) which may Specifically, transcranial electric stimulation ( TES ; Annar be captured via EEG or MEG . The neural oscillations umma et al . [ Annarumma L , D'Atri A , Alfonsi V , De associated with attention have been shown to be disrupted in Gennaro L The Efficacy of Transcranial Current Stimulation a number of conditions including epilepsy ( Besle et al . , Techniques to Modulate Resting - State EEG , to Affect Vigi [ Besle J , Schevon E A Mehta A D , Lakatos P, Goodman R lance and to Promote Sleepiness . Brain Sci . 2018 ; 8 ( 7 ) , 137 . R , McKhann G M , Emerson RG , Schroeder, C E. Tuning of doi.org/10.3390/brainsci8070137 ]) , including transcranial the human neocortex to the temporal dynamics of attended alternating current stimulation (tACS ) and transcranial events . The Journal of Neuroscience : The Official Journal of direct current stimulation ( DCS ; Utz et al. [Utz KS , the Society for Neuroscience 2011 ; 31 ( 9 ) 3176-3185 . doi . Dimova V , Oppenlander K Kerkhoff G. Electrified minds : org / 10.1523 / JNEUROSCI.4518-10.2011 ]) , dyslexia transcranial direct current stimulation ( DCS ) and galvanic ( Thomson et al . [ Thomson J M , Goswami U. Rhythmic vestibular stimulation (GVS ) as methods of non - invasive processing in children with developmental dyslexia : audi brain stimulation in neuropsychologya review of current tory and motor rhythms link to reading and spelling . Journal data and future implications. Neuropsychologia 2010 ; of Physiology , Paris 2008 ; 102 ( 1-3 ) , 120-129 . doi.org/10 . 48 ( 10 ) , 2789-2810 ] ) are used to electrically stimulate corti 1016 / j.jphysparis.2008.03.007 ]; Leong et al . [ Leong V , Gos cal activity , while transcranial magnetic stimulation ( TMS ; wami U. Assessment of rhythmic entrainment at multiple see ( Lawson McLean A. Publication trends in transcranial timescales in dyslexia : evidence for disruption to syllable magnetic stimulation : a 30 - year panorama. Brain Stimula timing . Hearing Research 2014 ; 308 , 141-161 . doi.org/10 . tion 2019 ; in press . doi.org/10.1016/j.brs.2019.01.002 ]) uses 1016 / j.heares.2013.07.015 ]; Soltesz et al . [ Soltesz F , Szucs a precise magnetic field in order to achieve a similar D , Leong V , White S , Goswami U. Differential entrainment endpoint of electric current control. Typical brain entrain of neuroelectric delta oscillations in developmental dyslexia . ment methods utilize a constant stimulus ( e.g. , tDCS ) or a PloS One 2013 ; 8 ( 10 ) , e76608 . doi.org/10.1371/journal . synthetic waveform , which may be a step function modu pone.0076608 ] ), and schizophrenia ( Lakatos et al . [ Lakatos lated on a direct current ( such as “ electrosleep” [Robino P , Schroeder CE , Leitman D I , Javilt D C. Predictive vitch L G. Electric analgesia, and electric resuscitation after suppression of cortical excitability and its deficit in schizo head failure under chloroform or electrocution . Journal of phrenia. The Journal of Neuroscience: The Official Journal the American Medical Association 1911 ; LVI ( 7 ) ,478-481 . of the Society for Neuroscience 2013 ; 33 ( 28 ) , 11692-11702 . doi.org/10.1001/jama.1911.02560070010003 ]) , a sinusoid doi.org/10.1523/JNEUROSCI.0010-13.2013 ] ). Therefore , modulated on an oscillatory direct current ( osc - DCS ; the acquisition of a brainwave signature during states of D’Atri et al . [ D’Atri A , DeSimoni E , Gorgoni M , Ferrara M , attention in a healthy “ donor ” may prove valuable when Ferlazzo F , Rossini P M , De Gennaro L Electrical stimula applied to a recipient exhibiting attention deficits associated tion of the frontal cortex enhances slow - frequency EEG with disrupted or otherwise irregular cortical oscillations . activity and sleepiness. Neuroscience 2016 ; 324 , 119-130 . Previous research shows that memory functions are acutely doi.org/10.1016/j.neuroscience.2016.03.007 ] ), or a fixed sensitive to neural entrainment and may be disrupted via frequency modulated on alternating current ( tACS ; [ Rosa , TMS (Hanslmayr et al . [ Hanslmayr S , Matusche k J , Fellner MA , Lisanby, S H. Somatic treatments for mood disorders. M - C Entrainment of Prefrontal Beta Oscillations Induces an Neuropsychopharmacology 2012 ; 37 ( 1 ) , 102-16.10.1038 / Endogenous Echo and Impairs Memory Formation . Current npp.2011.225 ] ). Helfrich et al . , supra , utilized simultaneous Biology 2014 ; 24 ( 8 ), 904-909. doi.org/10.1016/j.cub.2014 . LACS stimulation combined with EEG recordings to show 03.007 ] ) indicating the possibility of an inverse , positive that, when tACS was applied to the parieto -occipital lobe of entrainment of these oscillations . the brain , alpha wave activity increased and became syn [ 0155 ] Similarly, emotional arousal and valence are cor chronized with the entrainment frequency. related with distinct cortical signatures observable through [ 0153 ] These techniques may be used in order to record EEG (Allen et al . [ Allen J J B , Keune P M , Schönenberg M , and subsequently induce specific brain states . Thus, EEG / Nusslock R. Frontal EEG alpha asymmetry and emotion : MEG may be used to record mental states , which may then From neural underpinnings and methodological consider be applied via TES ( UDCS , OSC - DCS, TACS ) or TMS in ations to psychopathology and social cognition . Psycho order to replicate the cognitive -behavioral state of the physiology 2018 ; 55 ( 1 ) . doi.org/10.1111/psyp.13028 ] ). Pre “ donor . " This technique has been previously investigated in vious data indicate that happiness resultant of musical the domain of sleep [Gebodh N , Vacchi L , Adair D , Unal , experience, for instance , is associated with increased theta Poltorak A , Poltora k V , Bikson M. Proceedings # 11 : Replay frequency oscillations in the left frontal cortical hemisphere of Endogenous Sleep Rhythms to Produce Sleepiness. Brain ( Rogenmoser et al . [ Rogenmosert, Zollinger N , Elmer S , Stimulation : Basic , Translational, and Clinical Research in Jancke L Independent component processes underlying Neuromodulation 2019 ; 12 ( 2 ) , e71 - e72 . doi.org/10.1016/j. emotions during natural music listening . Social Cognitive brs.2018.12.180 ] . and Affective Neuroscience 2016 ; 11 ( 9 ) , 1428-1439 . doi.org/ [ 0154 ] The present technology involves, according to one 10.1093 / scan / nsw048 ] ). Cortical oscillations associated embodiment, the notion of “ transplanting ” mental states with negative affect conversely correlate with decreased US 2020/0368491 A1 Nov. 26 , 2020 22 theta frequency oscillations in this same region . Notably, temporally dynamic signals are more likely to induce natu aberrant cortical oscillations have been observed in a range ralistic mind - states holistically . This technique may , there of affective disorders including major depression ( Van der fore, provide a novel approach to the non - invasive treatment Vinne et al . [ Van der Vinne N , Vollebregt MA , van Puffen of a variety of disorders whose current treatments are limited MJAM , Arns M. Frontal alpha asymmetry as a diagnostic to pharmacotherapeutic interventions. marker in depression : Fact or fiction ? A meta - analysis . [ 0157 ] Sleep disorders affect a significant portion of the Neurolmage . Clinical 2017 ; 16 , 79-87 . doi.org/10.1016/j. adult population . Between 50 and 70 million adults in the nic1.2017.07.006 ] ) . Indeed , the left frontal hemisphere U.S. have a sleep disorder . ( Ohayon M M. Epidemiology of exhibits disrupted cortical rhythms in patients diagnosed insomnia : what we know and what we still need to learn . with major depression as compared to healthy controls Sleep medicine reviews. 2002 ; 6 ( 2 ) : 97-111 . ) Insomnia is the ( Nusslock et al . [Nusslock R , Shackman AI, McMenamin B most common specific sleep disorder, with short- term issues W , Greischar L L , Davidson R J , Kovacs M. Comorbid reported by about 30 % of adults and chronic insomnia by anxiety moderates the relationship between depression his 10 % . ( Kessler R C , Berglund P A Coulouvrat C , et al . tory and prefrontal EEG asymmetry . Psychophysiology Insomnia and the performance of US workers : results from 2018 ; 55 ( 1 ) . doi.org/10.1111/psyp.12953 ]) . Similar data the America insomnia survey . Sleep . 2011 ; 34 ( 9 ) 1161-1171 ; have highlighted cortical asymmetry of frontal lobe oscil Sateia M I , Doghramji K Hauri PI , Morin C M. Evaluation lations in post -traumatic stress disorder ( MD ; Meyer et al . of chronic insomnia . An American Academy of Sleep Medi [Meyer T, Quaedflieg C WE M , Weijland K Schruers K cine review . Sleep . 2000 ; 23 ( 2 ) : 243-308 . ) Chronic insomnia Merckelbach H , Smeets T. Frontal EEG asymmetry during is associated with deterioration of memory , adverse effects symptom provocation predicts subjective responses to intru on endocrine functions and immune responses , and an sions in survivors with and without MD . Psychophysiology increase in the risk of obesity and diabetes Sateia et al . 2000 ; 2018 ; 55 ( 1 ) . doi.org/10.1111/psyp.12779 ] ). Simple cortical Taylor DJ , Mallory U , Lichstein KL , Durrence H H , Riedel entrainment via binaural beat stimulation has already proven B W , Bush A I. Comorbidity of chronic insomnia with adequate for inducing specific emotional states ( Chaieb et al . medical problems. Sleep . 2007 ; 30 ( 2 ) : 213-218 ) . While at [ Chaieb L , Wilped E C , Reber T P , Felli Auditory Beat any age , managing insomnia is a challenge , it is especially Stimulation and its Effects on Cognition and Mood States. a critical condition in the elderly due to age - related increases Frontiers in Psychiatry 2015 ; 6. doi.org/10.3389/fpsyt.2015 . in comorbid medical conditions and medication use , as well 00070 ] ) . More directly , cranial electrotherapy has been as age - related changes in sleep structure , which shorten demonstrated as an efficacious treatment for depression , sleep time and impair sleep quality. ( Ancoli - Israel S. Insom anxiety, and certain forms of insomnia ( Kirsch et al . [ Kirsch nia in the elderly: a review for the primary care practitioner. D L , Nichols F. Cranial Electrotherapy Stimulation for Sleep . 2000 ; 23 : 523-30 ; discussion 536-28 ; Buysse D J. Treatment of Anxiety, Depression , and Insomnia . Psychiat Insomnia , depression , and aging. Assessing sleep and mood ric Clinics of North America 2013 ; 36 ( 1 ) , 169-176 . doi . interactions in older adults. Geriatrics ( Basel , Switzerland ). org / 10.1016 /j.psc2013.01.006 ] ). Certain forms of depres 2004 ; 59 ( 2 ) : 47-51 ; quiz 52. ) As a result, decreased sleep sion may respond better to transcranial approaches, such as quality is one of the most common health complaints of TMS , as has been demonstrated in early data on patients older adults . Medications are widely prescribed for relief with treatment -resistant major depression ( Rosenberg et al . from insomnia . However, sleep -promoting agents, such as [ Rosenberg O , Shoenfeld N , Zangen A , Kotler M , Dannon hypnotic drugs, can produce adverse effects, particularly in P N. Deep TMS in a resistant major depressive disorder a the elderly. ( Sateia M J , Buysse D J , Krystal A D , Neubauer brief report. Depression and Anxiety 2010 ; 27 ( 5 ) , 465-469 . D N. Adverse Effects of Hypnotic Medications . J Clin Sleep doi.org/10.1002/da.206891) . Med . Jun . 15 , 2017 ; 13 ( 6 ) : 839 . ) Even natural supplements, [ 0156 ] This approach to “ transplant " ( transfer ) mental such as melatonin , can cause some side effects, including states by replicating neural correlates of the donors state in headache, depression , daytime sleepiness, dizziness, stom a recipient is founded on two main principles. First , a large ach cramps, and irritability. ( Buscemi N , Vandermeer B , body of literature has identified distinct , measurable cortical Hooton N , et al . The efficacy and safety of exogenous signatures associated with specific brain - states ranging from melatonin for primary sleep disorders a meta - analysis. Jour those defining the sleep /wake cycle to those underlying nal of general internal medicine . 2005 ; 20 ( 12 ) : 1151-1158 . ) emotional experience . Second , TES and TMS have been [ 0158 ] Aside from the general deterioration of sleep qual repeatedly demonstrated as efficacious , safe means by which ity with age in adult population , the deterioration in quantity cortical rhythms may be entrained with a high degree of and quality of the slow - wave sleep ( SWS ) , which is non location - specificity. Together, these findings provide the REM deep sleep , is particularly troubling . (Roth T. Slow basis for the hypothesis that mental states can be “ trans wave sleep : does it matter ? Journal of clinical sleep medi planted ” ( transferred ) and provide the means by which a cine : JCSM : official publication of the American Academy cortical signature may be obtained via EEG or EMG asso of Sleep Medicine. 2009 ; 5 ( 2 Suppl ) : 54 ) . SINS plays an ciated with a desired mental state of a “ donor ” that may , in important role in cerebral restoration and recovery in turn , be processed, inverted , and subsequently applied to a humans . Studies have shown that a 15 % reduction in the recipient in order to induce said cognitive state via cortical amounts of SWS and increased number and duration of rhythm entrainment using tACS , TMS or other stimuli such awakenings are associated with normal aging . ( Chinoy E D , as light or sound . Theoretical considerations suggest that this Frey D J , Kaslovsky D N , Meyer F G , Wrightlr K P. hypothesis is plausible and deserves experimental verifica Age - related changes in slow wave activity rise time and tion . Importantly , using cortical signatures acquired from a NREM sleep EEG with and without zolpidem in healthy “ donor , ” rather than a fixed - frequency or synthetic wave young and older adults. Sleep medicine . 2014 ; 15 ( 9 ) : 1037 form applications as is currently typical for TES techniques, 1045. ) Experimental disruption of SWS have been shown to offers the distinct advantage of replicating mufti -phasic , increase shallow sleep , sleep fragmentation , daytime sleep US 2020/0368491 A1 Nov. 26 , 2020 23 propensity, and impair daytime function . (Dijk DJ . Regula research that aimed at comprehensive identification of all tion and functional correlates of slow wave sleep . J Clin independent components of EEG signals during sleep ; and Sleep Med . Apr. 15 2009 ; 5 ( 2 Suppl) : 56-15 ; Restoring comprehensive analysis of statistically significant inter -de Deep , Slow Wave Sleep to Enhance Health and Increase pendence of a presence of an independent component with Lifespan , nutritionreview.org/2014/07/restoring-slow-wave the particular stage of sleep . Comprehensive identification sleep - shown - enhance - health - increase -lifespan ( 2014 ) ) . and analysis of independent components associated with Given that SWS contributes to sleep continuity , enhance sleep would allow to use those components and / or derived ment of SWS may lead to improvements in sleep quality and signals for a tACS protocol daytime function in patients with insomnia and the elderly . [ 0161 ] EEG recordings of brainwaves are obtained and Furthermore, accumulating evidence point to the SWS is the pre - processed from healthy human subjects during various time when short- term memory is consolidated into long stages of sleep . EEG recordings of three stages of sleep , and term memory . ( Born J. Slow -wave sleep and the consolida while being awake from at least ten healthy subjects ( e.g. , tion of long - term memory . The World Journal of Biological through public EEG database ), which are then smoothed and Psychiatry. 2010 ; 11 ( supl) : 16-21 .) Recent research connects filtered . The EEG recordings are analyzed to identify sta the deterioration of the SWS with early onset of Alzheimer's tistically significant waveform components correlated with disease and other forms of dementia . (Petit D , Gagnon J - F , specific sleep stages . A model ( e.g. , a linear multivariate Fantini M L , Ferini - Strambi Montplaisir J. Sleep and quan model ) is developed for the coefficients of the components titative EEG in neurodegenerative disorders . Journal of of the EEG , based on sleep stage /wakefulness status ; and the psychosomatic research . 2004 ; 56 ( 5 ) : 487-496 ; McCurry S statistical significance of the model is measured . Stimulation M , Ancoli- Israel S. Sleep dysfunction in Alzheimer's dis protocols are developed that can provide safe and effective ease and other dementias . Current treatment options in neurostimulation to induce desired sleep stage . neurology. 2003 ; 5 ( 3 ) : 261-272 ) . It is also suggested that the [ 0162 ] Great economic burden and the societal cost is loss of SWS stage may play a role in these debilitating incurred due to sleeping disorders, particularly insomnia . age -related diseases . (Mattis J , Sehgal A. Circadian rhythms, Sleep disturbances are common symptoms in adults and are sleep , and disorders of aging. Trends in Endocrinology Er related to various factors, including the use of caffeine, Metabolism . 2016 ; 27 (4 ): 192-203 ). Unfortunately, most tobacco , and alcohol ; sleep habits; and comorbid diseases/ . standard sleeping pills , while alleviating insomnia , do little [ 0163 ] Epidemiologic studies indicate sleep disorders are to improve the SWS . ( Walsh R Enhancement of slow wave affecting a significant portion of adult population . Between sleep : implications for insomnia . Journal of clinical sleep 50 and 70 million adults in the U.S. have a sleep disorder . medicine: JCSM : official publication of the American Acad Insomnia is the most common specific sleep disorder, with emy of Sleep Medicine . 2009 ; 5 ( 2 Suppl ) : 527 . ) Some evi short - term issues reported by about 30 % of adults and dence suggests that some hypnotic drugs change the struc chronic insomnia by 10 % . ( Kessler et al . ( 2011 ) , Sateia et al . ture of sleep , adversely affecting the SWS ( Sateia et al . ( 2000 ) , Ancoli - Israel et al . ( 2000 ) , Ancoli - Israel S , Roth T. ( 2017 ) ; Walsh ( 2009 ) . Hence , there is an unmet need for Characteristics of insomnia in the United States : results of non - pharmacological techniques for promoting sleep , par the 1991 National Sleep Foundation Survey. I. Sleep . 1999 ; ticularly , the deep non -REM sleep stage ( SWS ) lacking in 22 : 5347-353 ) . Chronic insomnia is associated with deterio the elderly population . ration of memory, adverse effects on endocrine functions [ 0159 ] One of the promising non - pharmacological and immune responses , and an increase in the risk of obesity approaches to promoting sleep is neuromodulation via light, and diabetes . ( Sateia et al . ( 2000 ) ) . In addition , there is a sound , and / or transcranial electric Stimulation ( TES ) . Lim significant economic burden and societal cost associated ited human trials conducted by Neuroenhancement Lab in with insomnia due to the impact on health care utilization , collaboration with the Neuromodulation Laboratory at The impact in the work domain , and quality of life . Recent City College of New York ( CUNY ) showed promise in estimates of direct and indirect costs are upwards of 100 replicating the desired sleep stage 0 a healthy donor in other billion dollars annually in the United States . ( Fullerton DS . subjects ( recipients ). Electroencephalogram ( EEG ) of The economic impact of insomnia in managed care : a dearer healthy volunteers were recorded as they dozed off entering picture emerges. Am J Manag Care . May 2006 ; 12 ( 8 Suppl) : stage 1 of sleep , as evidenced by the predominance of alpha S246-252 . ) While at any age , managing insomnia is a waves . These EEG recordings were subsequently filtered challenge, it is especially a critical condition in the elderly from noise , inverted , and used for transcranial Endogenous due to age - related increases in comorbid medical conditions Sleep -Derived stimulation ( ESD ) . Volunteer subjects stimu and medication use , as well as age - related changes in sleep lated with tESD modulated with the indigenous brainwaves structure , which shorten sleep time and impair sleep quality. recorded in a sleeping donor, quickly dozed off and entered ( Ancoli- Israel ( 2000 ) . As a result , decreased subjective sleep stage 1 of sleep , as evidenced by EEG , heart rate , respiration quality is one of the most common health complaints of rate , and post -sleep cognitive test . These results were better older adults . as compared to the control arms of the study that included [ 0164 ] There is a deterioration of the slow - wave sleep sham stimulation , DCS, and tACS ( 10 Hz ) . These prelimi ( SWS ) in the elderly. Aside from the general deterioration of nary results suggest that tACS modulated with indigenous sleep quality with age in the adult population , the deterio brainwaves recorded from a healthy sleeping donor can be ration in quantity and quality of the slow -wave sleep ( SWS ) , used to replicate the desired sleep stage of a healthy donor which is the deep non - REM sleep , is particularly troubling . in another subject. (Roth ( 2009 ) ) . SWS plays an important role in cerebral [ 0160 ] There is significant research to identify markers of restoration and recovery in humans . It is the most prominent different phases of healthy or pathological sleep ; the markers EEG event during sleep and appears as spontaneous large allow to classify observed EEG to one of the phases of oscillations of the EEG signal occurring approximately once sleep /wake categories . The applicants are not aware of any every second in the deepest stage of non - REM sleep . US 2020/0368491 A1 Nov. 26 , 2020 24

( Achermann P , Dijk D - J , Brunner D P , Borbély AA . A model electrodes or caps with arrays of electrodes, e.g. , 20-256 of human sleep homeostasis based on EEG slow - wave electrodes positioned on the scalp . However, in some cases , activity : quantitative comparison of data and simulations. especially where high spatial resolution is not required, and Brain research bulletin . 1993 ; 31 ( 1-2 ) : 97-113 . ) Studies have dominant brainwave patterns are sought, simpler and less shown that a significant decrease ( ~ 15 % reduction ) in the controlled EEG acquisition systems may be employed , amounts of SWS and increased number and duration of including through commercially available device intended awakenings are associated with normal aging . ( Chinoy et al . to interface with smartphones. See , kokoon.io , www.think ( 2014 ) ) . Given that SWS contributes to sleep continuity and mindsetcom /; www.choosemuse.com ( Muse , Muse2 ) ; Neu experimental disruption of SWS increases shallow sleep and rosky; getvi.com Sense ) ; Strickland , Eliza , “ In - Ear EEG sleep fragmentation , enhances daytime sleep propensity , and Makes Unobtrusive Brain - Hacking Gadgets a Real Possi impairs daytime function . (Dijk ( 2009 ) ; Nutrition Review . bility ” , IEEE Spectrum Jul . 7 , 2016 ; Strickland , Eliza , org ( 2014 ) ) , enhancement of SWS may lead to improve “ Wireless Earbuds Will Record Your EEG , Send Brainwave ments in sleep maintenance and daytime function in patients Data To Your Phone ” , IEEE Spectrum May 17 , 2016. The with insomnia and in the elderly. Furthermore, accumulating Unicorn “ Hybrid Black ” wearable EEG headset provides a evidence point to the SWS as the time when short - term headset with eight electrode channels and digital data acqui memory is consolidated into long - term memory . ( Born sition electronics ( 24 bit 250 Hz ) , intended to provide a ( 2010 ) ) . Recent research connects the deterioration of the brain -computer interface for a distic , control and other tasks . SWS with early onset of AlzheimeCs disease and other See , www.unicorn-bi.com/. Starkey Laboratories , Inc. US forms of dementia . ( Petit et al . ( 2010 ) ; McCurry et al . 20190166434 discloses an ear - worn electronic device hav ( 2003 ) ) . It is also suggested that the loss of SWS stage may ing a plurality of sensors for EEG signals from a wearer's be the culprit for these debilitating age -related diseases . ear , as a brain - computer interface . A number of designs (Mattis et al . ( 2016 ) ) . provide in - ear headphones which integrate EEG electrodes [ 0165 ] SWS enhancement is a potential non -pharmaco that pick up signals from the ear canal. See Reference List logical therapy for the elderly. Given the pivotal role of slow Table 18 . waves during sleep , it is not surprising that several efforts [ 0167 ] The brain activity of a first subject ( a “ donor” who have been made to increase sleep efficacy by potentiating is in the desired sleeping state ) may be captured by recording SWS . Recently, a number of drugs have been shown to neural correlates of the sleep , as expressed by brain activity increase SWS . Although acting on different synaptic sites , patterns, such as EEG signals. The representations of the overall the slow wave enhancing the effect of these drugs is neural correlates of the first subject are used to control mediated by enhancing GABAergic transmission . Specifi stimulation of a second subject ( a “ recipient ” ) , seeking to cally, clinical investigations showed that both tiagabine and induce the same brain activity patterns of the donor in the gaboxadol increased the duration of SWS after sleep restric recipient to assist the recipient in attaining the desired sleep tion . ( Walsh ( 2009 ) ; Mathias S , Wetter T C , Steiger A , state that had been attained by the donor. Lancet M. The GABA uptake inhibitor tiagabine promotes [ 0168 ] One strategy to enhance deep sleep non -pharma slow wave sleep in normal elderly subjects. Neurobiology of cologically is to stimulate the brain with light, sound , aging . 2001 ; 22 ( 2 ) : 247-253 ; Walsh J K , Snyder E , Hall J , et electrical currents , or magnetic fields based on artificial and al . Slow wave sleep enhancement with gaboxadol reduces synthetic stimulation paradigms. Intermittent transcranial daytime sleepiness during sleep restriction . Sleep . May direct- current stimulation ( DCS ) applied at 0.75 Hz for 2008 ; 31 ( 5 ) : 659-672 ; Feld GB , Wilhelm I , Ma Y , et al . Slow 5 - min intervals separated by 1 - min off periods after SWS wave sleep induced by GABA agonist tiagabine fails to onset can increase the EEG power in the slow oscillation benefit memory consolidation . Sleep . Sep. 1 , 2013 ; 36 ( 9 ) : band ( < 1 Hz ) during the stimulation - free intervals . (Lang N , 1317-1326 ) . Tiagabine also improved performance on cog Siebner HR , Ward N S , et al . How does transcranial DC nitive tasks evaluating executive functions and reduced the stimulation of the primary motor cortex alter regional neu negative effects of sleep restriction on alertness. (Walsh J K , ronal activity in the human brain ? European Journal of Randazzo A C , Stone K et al . Tiagabine is associated with Neuroscience . 2005 ; 22 ( 2 ) : 495-504 ; Marshall L , Helgadoltir sustained attention during sleep restriction : evidence for the H , Molle M , Born J. Boosting slow oscillations during sleep value of slow - wave sleep enhancement ? Sleep - New York potentiates memory. Nature . Nov. 30 2006 ; 444 (7119 ) : 610 Then Westchester- . 2006 ; 29 ( 4 ) : 433 ) . Although these results 613 ) . Similarly, stimulated by tDCS at the beginning of are positive, pharmacological approaches to sleep enhance SWS accelerate the SWA homeostatic decay in subjects. ment often raise issues related to dependence and tolerance ( Reato D , Gasca F , Datta A , Bikson M , Marshall L , Parra L and are commonly associated with residual daytime side C. Transcranial electrical stimulation accelerates human effects . Some evidence suggests that some hypnotic drugs, sleep homeostasis . PLoS Comput Biol . 2013 ; 9 ( 2 ) : while alleviating insomnia , change the structure of sleep e1002898 ) . Furthermore , slow waves can be triggered by adversely affecting the SWS . ( Sateia ( 2000 ) ; Walsh ( 2009 ) ) . directly perturbing the cortex during non - REM sleep using Even natural supplements, such as melatonin , can cause transcranial magnetic stimulation ( TMS ) . (Massimini M , some side effects, including headache, short - term feelings of Ferrarelli F , Esser S K , et al . Triggering sleep slow waves by depression , daytime sleepiness, dizziness , stomach cramps , transcranial magnetic stimulation . Proc Natl Acad Sci USA . and irritability. ( Buscemi et al . ( 2005 ) ) . Hence , there is an May 15 2007 ; 104 ( 20 ) : 8496-8501 ) . Other research has unmet need for a non -pharmacological technique for pro focused on the possibility of inducing slow waves in a more moting sleep , particularly in the deep non - REM sleep stage physiological natural manner. In a larger study in healthy lacking in the elderly population . adults, bilateral electrical stimulation of the vestibular appa [ 0166 ] Brainwaves , e.g. , EEG signals , may be acquired in ratus shortened sleep onset latency in comparison to sham various ways. Traditional signal acquisition by neurologists nights where no stimulation was provided. ( Krystal A D , and encephalography / EEG technicians involves pasted - on Zammit G K Wyaff J K , et al . The effect of vestibular US 2020/0368491 A1 Nov. 26 , 2020 25 stimulation in a four - hour sleep phase advance model of [ 0171 ] Sleep is a natural periodic suspension of conscious transient insomnia . J Clin Sleep Med . Aug. 15 2010 ; 6 ( 4 ) ness , basically a process that can hardly be influenced in its 315-321 ) . The effect of somatosensory and auditory stimu individual stages by the person sleeping . It is a subconscious lation was also assessed (Krystal et al . 2010 ; Ngo H V , Ma ( in a technical sense ) mental state, representing a resting rtinetz T , Born J , Molle M. Auditory closed - loop stimulation state , activity pattern , activity rhythm , readiness , receptivity , of the sleep slow oscillation enhances memory. Neuron . or other state , often independent of particular inputs. In May 8 2013 ; 78 ( 3 ) : 545-553 ) . While the change observed essence , a sleep state in a particular sleep stage or a sequence with somatosensory stimulation was minor, acoustic stimu of different sleep stages of the first subject ( a “ donor ” who lation was particularly efficacious in enhancing sleep slow is in a desired sleep stage or goes through a sequence with waves . Specifically, using an intermittent stimulation , in its individual stages ) is captured by recording neural corre which tones were played in blocks of 15 s spaced out by lates of the sleep state , e.g. , as expressed by brain activity stimulation - free intervals, slow waves appeared remarkably patterns, such as EEG or MEG signals. The neural correlates large and numerous during the stimulation blocks . ( Tononi of the first subject, either as direct or recorded representa G , Riedner B , Hulse B , Ferrarelli F , Sarasso S. Enhancing tions , may then be used to control a stimulation of the second sleep slow waves with natural stimuli. Medicamundi. 2010 ; subject ( a “ recipient ” ), seeking to induce the same brain 54 ( 2 ) : 73-79 ; Bellesi M , Riedner B A Garcia -Molina G N , activity patterns in the second subject ( recipient) as were Cirelli C , Tononi G. Enhancement of sleep slow waves : present in the first subject ( donor ), thereby transplanting the underlying mechanisms and practical consequences . Fron sleep state of the first subject ( donor ), to assist the second tiers in systems neuroscience . 2014 ; 8 : 208 ) . In addition , subject (recipient ) to attain the desired sleep stage that had high -density EEG studies ( hdEEG , 256 channels ) showed been attained by the donor . In an alternative embodiment, that the morphology, topography, and traveling patterns of the signals from the first subject ( donor ) being in a first sleep induced slow waves were indistinguishable from those of stage are employed to prevent the second subject ( recipient ) spontaneous slow waves observed during natural sleep . A from achieving a second sleep stage , wherein the second recent study found that EEG SWA increased following tone sleep stage is an undesirable one . Furthermore, the duration presentation during non - REM sleep ( Arzi A , Shedlesky L , and timing of different sleep stages can be controlled in the Ben - Shaul M , et al . Humans can learn new information second subject. This could enable the change of the indi during sleep . Nature neuroscience . 2012 ; 15 ( 10 ) : 1460 ) , and vidual duration or intensity of each sleep stage and the order slow oscillation activity ( 0.5-1 Hz ) was increased in in which they appear. In some embodiments, the signals response to continuous acoustic stimulation at 0.8 Hz start from the first subject can be used to trigger sleep in the ing 2 min before lights were turned off and lasting for 90 second subject or to prevent sleep or sleepiness and asso min . (Ngo H V , Claussen JC , Born J , Molle M. Induction of ciated symptoms such as fatigue, lack of concentration , etc. slow oscillations by rhythmic acoustic stimulation . J Sleep [ 0172 ] In one embodiment, brain activity patterns are Res . February 2013 ; 22 ( 1 ) : 22-31 ) . Unlike the previous neu recorded during a complete sleep cycle or during several rostimulation methods with a dificialand synthetic stimula such cycles over the course of a normal night sleeping . In tion paradigms, the present stimulation protocol uses source some embodiments, the acquiring of the sleep state infor derived waveforms, extracted from the indigenous brain mation is preceded by or followed by identifying the sleep activity EEG recordings of the healthy subjects, processed stage , by direct reporting by the first subject ( donor ) or a n by statistical methods ( e.g. , principal component analysis , observer, or by automated analysis of the physiological independent component analysis ( Ungureanu M , Bigan C , parameters ( e.g. , brain activity patterns, heartbeat, breathing Strungaru R , Lazarescu V. Independent component analysis pattern , oxygen saturation in blood , temperature , eye move applied in biomedical signal processing . Measurement Sci ment, skin impedance , etc. ) or both . In other embodiments, ence Review . 2004 ; 4 ( 2 ) : 18 ) or spatial principal component the processing of the brain activity patterns does not seek to analysis, autocorrelation, etc. ) , which separates components classify or characterize it , but rather to filter and transform of brain activity. These separated brain EEG activities are the information to a form suitable for control of the stimu then modified or modulated and subsequently inverted and lation of the second subject. In particular, according to this used for transcranial Endogenous Sleep - Derived stimulation embodiment, the subtleties that are not yet reliably classified ( ESD ). The application of endogenous brain waveform in traditional brain activity pattern analysis are respected. should not only retain the efficacy in triggering SINS but For example, it is understood that all brain activity is also alleviate the safety concerns that are associated with reflected in synaptic currents and other neural modulation long - term brain stimulation using synthetic paradigms. and , therefore, theoretically , conscious and subconscious information is , in theory, accessible through brain activity [ 0169 ] The present technology provides a method of pattern analysis . Since the available processing technology improving sleep by transplanting sleep states- one desired generally fails to distinguish a large number of different sleep stage , or the sequences of sleep stages — from the first brain activity patterns, that available processing technology , subject ( donor ) ( or from a plurality of donors ) to a second is necessarily deficient, but improving . However, just subject ( recipient ). ( In some embodiments , the first and the because a computational algorithm is unavailable to extract second subject may be the same subject at different points in the information , does not mean that the information is time , or based on a protocol or algorithm .) absent. Therefore, this embodiment employs relatively raw [ 0170 ] The process seeks to achieve , in the subject, a brain activity pattern data , such as filtered or unfiltered brainwave pattern , which is derived from a human . The EEGs , to control the stimulation of the second subject, brainwave pattern is complex, representing a superposition without a full comprehension or understanding of exactly of modulated waveforms. The modulation preferably is what information of significance is present. In one embodi determined based on brain wave patterns of another subject ment, brainwaves are recorded and “ played back ” to another or plurality of subjects . subject, similar to recording and playing back music . Such US 2020/0368491 A1 Nov. 26 , 2020 26 recording - playback may be digital or analog. Typically, the activity is appropriate for the circumstances . Further, in stimulation may include a low dimensionality stimulus, such some cases , a single mental state , emotion or mood is not as stereo -optic binaural, isotonic tones , tactile , or other described or fully characterized , and therefore acquiring sensory stimulation, operating bilaterally , and with control signals from a source is an efficient exercise . over frequency and phase and / or waveform and / or transcra [ 0177 ] With a library of target brainwave patterns, a nial stimulation such as TES , DCS, HD - tDCS , ACS, or system and method is provided in which a target subject may TMS . A plurality of different types of stimulation may be be immersed in a presentation , which includes not only applied concurrently , e.g. , visual , auditory, other sensory , multimedia content but also a series of defined mental states , magnetic , electrical. emotional states or moods that accompany the multimedia [ 0173 ] Likewise, a present lack of complete understanding content. In this way, the multimedia presentation becomes of the essential characteristics of the signal components in fully immersive. The stimulus, in this case , may be provided the brain activity patterns does not prevent their acquisition , through a headset, such as a virtual reality or augmented storage, communication, and processing ( to some extent ). reality headset . This headset is provided with a stereoscopic The stimulation may be direct, i.e. , a visual , auditory, or display , binaural audio , and a set of EEG stimulatory elec tactile stimulus corresponding to the brain activity pattern or trodes . These electrodes ( if provided ) typically deliver a a derivative or feedback control based on the second sub subthreshold signal , which is not painful, which is typically jects brain activity pattern . an AC signal which corresponds to the desired frequency, [ 0174 ] To address the foregoing problems, in whole or in phase , and spatial location of the desired target pattern . The part, and / or other problems that may have been observed by electrodes may also be used to counteract undesired signals, persons skilled in the art , the present disclosure provides by destructively interfering with them while concurrently methods, processes , systems, apparatus, instruments, and / or imposing the desired patterns . The headset may also gener devices, as described by way of example in implementations ate visual and / or auditory signals which correspond to the set forth below . desired state . For example, the auditory signals may induce [ 0175 ] While mental states are typically considered inter binaural beats , which cause brainwave entrainment. The nal to the individual, and subjective such states are common visual signals may include intensity fluctuations or other across individuals and have determinable physiological and modulation patterns, especially those which are subliminal , electrophysiological population characteristics . Further , that are also adapted to cause brainwave entrainment or mental states may be externally changed or induced in a induction of the desired brainwave pattern . manner that bypasses the normal cognitive processes . In [ 0178 ] The headset preferably includes EEG electrodes some cases , the triggers for the mental state are subjective, for receiving feedback from the user. That is , the stimulatory and therefore the particular subject- dependent sensory or system seeks to achieve a mental state , emotion or mood excitation scheme required to induce a particular state will response from the user . The EEG electrodes permit deter differ. For example , olfactory stimulation can have different mination of whether that state is achieved , and if not , what effects on different people , based on differences in the the current state is . It may be that achieving the desired history of exposure, social and cultural norms, and the like . brainwave pattern is state dependent, and therefore that On the other hand, some mental state response triggers are characteristics of the stimulus to achieve the desired state normative , for example, “ tearjerker ” media . depend on the starting state of the subject. Other ways of [ 0176 ] Mental states are represented in brainwave pat determining mental state , emotion , or mood include analysis terns, and in normal humans, the brainwave patterns and of facial expression, electromyography ( EMG ) analysis of metabolic ( e.g. blood flow , omen consumption , etc. ) follow facial muscles , explicit user feedback , etc. prototypical patterns. Therefore, by monitoring brainwave [ 0179 ] An authoring system is provided which permits a patterns in an individual, a state or series of mental states in content designer to determine what mental states are desired , that person may be determined or estimated . However, the and then encode those states into media , which is then brainwave patterns may be interrelated with context, other interpreted by a media reproduction system in order to activity , and past history. Further, while prototypical patterns generate appropriate stimuli. As noted above , the stimuli may be observed , there are also individual variations in the may be audio , visual , multimedia, other senses , or electrical patterns . The brainwave patterns may include characteristic or magnetic brain stimulation , and therefore a VR headset spatial and temporal patterns indicative of mental state . The with transcranial electrical or magnetic stimulation is not brainwave signals of a person may be processed to extract required . Further, in some embodiments, the patterns may be these patterns, which , for example , may be represented as directly encoded into the audiovisual content, subliminally hemispheric signals within a frequency range of 3-100 Hz . encoded . In some cases , the target mental state may be These signals may then be synthesized or modulated into derived from an expert, actor or professional exemplar. The one or more stimulation signals , which are then employed to states may be read based on facial expressions , EMG , EEG , induce a corresponding mental state into a recipient, in a or other means, from the actor or exemplar. For example, a manner seeking to achieve a similar brainwave pattern from prototype exemplar engages in an activity that triggers a the source . The brainwave pattern to be introduced need not response, such as viewing the Grand Canyon or a rhNorks be newly acquired for each case . Rather, signals may be within the Louvre . The responses of the exemplar are then acquired from one or more individuals , to obtain an exem recorded or represented, and preferably brainwave patterns plar for various respective mental state . Once determined , recorded that represent the responses. A representation of the the processed signal representation may be stored in non same experience is then presented to the target, with a goal volatile memory for later use . However, in cases of complex of the target also experiencing the same experience as the interaction between a mental state and a context or content exemplar. This is typically a voluntary and disclosed pro or activity, it may be appropriate to derive the signals from cess , so the target will seek to willingly comply with the a single individual whose context or content - environment or desired experiences. In some cases , the use of the technol US 2020/0368491 A1 Nov. 26 , 2020 27 ogy is not disclosed to the target, for example in advertising [ 0186 ] The present technology may employ an event presentations or billboards. In order for an actor to serve as correlated EEG time and /or frequency analysis performed the exemplar, the emotions achieved by that person must be on neuronal activity patterns. In a time - analysis, the signal authentic . However, so - called “ method actors” do authenti is analyzed temporally and spatially, generally looking for cally achieve the emotions they convey . However, in some changes with respect to time and space . In a frequency cases , for example, where facial expressions are used as the analysis, over an epoch of analysis , the data , which is indicator of mental state , an actor can present desired facial typically a time -sequence of samples, is transformed , using expressions with inauthentic mental states . The act of mak e.g. , a Fourier transform ( FT, or one implementation , the ing a face corresponding to an emotion often achieves the Fast Fourier Transform , FFT ) , into a frequency domain targeted mental state . representation , and the frequencies present during the epoch [ 0180 ] In general, the present technology is directed are analyzed . The window of analysis may be rolling, and so toward inducing sleep . Note that certain kinds of content are the frequency analysis may be continuous. In a hybrid known to assist in induction of sleep , e.g. , a lulla bye . time - frequency analysis, for example, a wavelet analysis , However, the present technology encompasses both human the data during the epoch is transformed using a " wavelet comprehensible signals and incomprehensible ( noise - like ) transform ” , e.g. , the Discrete Wavelet Transform ( DWT ) or signals. continuous wavelet transform ( CWT ) , which has the ability to construct a time - frequency representation of a signal that [ 0181 ] In order to calibrate the system , the brain pattern of offers very good time and frequency localization . Changes in a person may be measured while in the desired state . The transformed data over time and space may be analyzed . In brain patterns acquired for calibration or feedback need not general, the spatial aspect of the brainwave analysis is be of the same quality, or precision , or data depth , and anatomically modeled . In most cases , anatomy is considered indeed may represent responses rather than primary indicia . universal, but in some cases , there are significant differ That is , there may be some asymmetry in the system , ences . For example, brain injury, psychiatric disease , age , between the brainwave patterns representative of mental race , native language, training, sex , handedness, and other state , and the stimulus patterns appropriate for inducing the factors may lead to distinct spatial arrangement of brain brain state . function, and therefore when transferring mood from one [ 0182 ] The present invention generally relates to achiev individual to another, it is preferred to normalize the brain ing a mental state in a subject by conveying to the brain of anatomy of both individuals by experiencing roughly the the subject patterns of brainwaves . These brainwaves may same experiences, and measuring spatial parameters of the be artificial or synthetic , or derived from the brain of a EEG or MEG . Note that spatial organization of the brain is second subject ( e.g. , a person experiencing an authentic highly persistent, absent injury or disease , and therefore, this experience or engaged in an activity ) . Typically, the wave need only be performed infrequently . However, since elec patterns of the second subject are derived while the second trode placement may be inexact, a spatial calibration may be subject is experiencing an authentic experience . performed after electrode placement. [ 0183 ] A special case is where the first and second subjects [ 0187 ] Different aspects of EEG magnitude and phase are the same individual. For example , brainwave patterns are relationships may be captured , to reveal details of the recorded while a subject is in a particular mental state . That neuronal activity . The " time - frequency analysis ” reveals the same pattern may assist in achieving the same mental state brain's parallel processing of information , with oscillations at another time . Thus, there may be a time delay between the at various frequencies within various regions of the brain acquisition of the brainwave information from the second reflecting multiple neural processes co - occurring and inter subject, and exposing the first subject to corresponding acting . See , Lis man J , Buzsaki G. A neural coding scheme stimulation . The signals may be recorded and transmitted . formed by the combined function of gamma and theta [ 0184 ] The temporal pattern may be conveyed or induced oscillations . Schizophr Bull . Jun . 16 , 2008 ; doi : 10.1093 / non - invasively via light ( visible or infrared ), sound ( or schbul / sbn060 . Such a time- frequency analysis may take the infrasound ). Alternately, non - sensory stimulation may be form of a wavelet transform analysis. This may be used to employed , e.g. , transcranial direct or alternating current assist in integrative and dynamically adaptive information stimulation ( tDCS or tACS ) , transcranial magnetic stimula processing. Of course , the transform may be essentially tion ( TMS ) , Deep transcranial magnetic stimulation ( Deep lossless and may be performed in any convenient informa TMS , or dTMS ), Repetitive Transcranial Magnetic Stimu tion domain representation . These EEG - based data analyses lation ( rTIMS) olfactory stimulation , tactile stimulation, or reveal the frequency - specific neuronal oscillations and their any other means capable of conveying frequency patterns. In synchronization in brain functions ranging from sensory a preferred embodiment, normal human senses are processing to higher - order cognition . Therefore , these pat employed to stimulate the subject, such as light, sound, terns may be selectively analyzed , for transfer to or induc smell , and touch . Combinations of stimuli may be tion in , a subject. employed . In some cases , the stimulus or combination is [ 0188 ] A statistical clustering analysis may be performed innate, and therefore largely pan - subject. In other cases , in high dimension space to isolate or segment regions which response to a context is learned , and therefore subject act as signal sources, and to characterize the coupling specific . Therefore, feedback from the subject may be appro between various regions. This analysis may also be used to priate to determine the triggers and stimuli appropriate to establish signal types within each brain region and decision achieve a mental state . boundaries characterizing transitions between different sig [ 0185 ] This technology may be advantageously used to nal types . These transitions may be state dependent, and enhance mental response to a stimulus or context. Still therefore the transitions may be detected based on a tem another aspect provides for a change in the mental state . The poral analysis, rather than merely a concurrent oscillator technology may be used in humans or animals . state . US 2020/0368491 A1 Nov. 26 , 2020 28

[ 0189 ] The various measures make use of the magnitude representing neural correlates of a sleep stage may have and / or phase angle information derived from the complex different relative differences between subjects. data extracted from the EEG during spectral decomposition [ 0194 ] Of course , in some cases , one or more components and / or temporal/ spatial/ spectral analysis. Some measures of the stimulation of the target subject ( recipient) may be estimate the magnitude or phase consistency of the EEG represented as abstract or semantically defined signals , and , within one channel across trials, whereas others estimate the more generally, the processing of the signals to define the consistency of the magnitude or phase differences between stimulation will involve high - level modulation or transfor channels across trials . Beyond these two families of calcu mation between the source signal received from the first lations , there are also measures that examine the coupling subject ( donor ) or plurality of donors , to define the target between frequencies, within trials and recording sites . Of signal for stimulation of the second subject ( recipient ). course , in the realm of time - frequency analysis , many types [ 0195 ] Preferably, each component represents a subset of of relationships can be examined beyond those already the neural correlates reflecting brain activity that has a high mentioned . autocorrelation in space and time , or in a hybrid represen [ 0190 ] These sensory processing specific neuronal oscil tation such as wavelet . These may be separated by optimal lations , e.g. , brainwave patterns, e.g. , of a subject ( a filtering ( e.g. , spatial PCA ) , once the characteristics of the " source ” ) or to a person trained ( for example, an actor signal are known, and bearing in mind that the signal is trained in the method ” ) to create the desired state , and can accompanied by a modulation pattern and that the two be stored on a tangible medium and / or can be simultane components themselves may have some weak coupling and ously conveyed to a recipient making use of the brain's interaction . frequency following response nature . See , Galbraith , Gary [ 0196 ] For example , if the first subject ( donor) is listening C , Darlene M. Off ma n , and Todd M. Huffman . “ Selective to music , there will be significant components of the neural attention affects human brain stem frequency - following correlates that are synchronized with the particular music . response .” Neuroreport 14 , no . 5 ( 2003 ) : 735-738 , journals. On the other hand, the music per se may not be part of the lww.com/neuroreport/Abstract/2003/04150/Selective_af desired stimulation of the target subject ( recipient ). Further, fention affects_human_brain_stem.15.aspx. the target subject ( recipient) may be in a different acoustic [ 0191 ] According to one embodiment, the stimulation of environment, and it may be appropriate to modify the the second subject is combined with a feedback process , to residual signal dependent on the acoustic environment of the verify that the second subject has appropriately responded to recipient, so that the stimulation is appropriate for achieving the stimulation, e.g. , has a predefined similarity to the sleep the desired effect, and does not represent phantoms, distrac stage as the first subject, has a sleep stage with a predefined tions , or irrelevant or inappropriate content. In order to difference from the first subject, or has the desired change perform signal processing, it is convenient to store the from a baseline sleep stage . The feedback may be based on signals or a partially processed representation , though a brain activity per se , or neural correlates of sleep stage or , complete real - time signal processing chain may be imple alternatively, or, in addition, physical, psychological , or mented . According to another embodiment, a particular behavioral effects that may be measured , reported or stage of the sleep state of at least one first subject ( donor) is observed . The feedback typically is provided to a controller identified , and the neural correlates of brain activity are with at least partial model basis , for the stimulator, which captured , and the second subject ( recipient) is subject to alters stimulation parameters to optimize the stimulation . stimulation based on the captured neural correlates and the [ 0192 ] As discussed above , the model is typically difficult identified sleep stage . The sleep stage is typically repre to define . Therefore , the model - based controller is incom sented as a semantic variable within a limited classification pletely defined , and the existence of errors and artifacts is to space . The sleep stage identification need not be through be expected . However, by employing a model - based con analysis of the neural correlates signal and may be a voli troller, those parameters that are defined may be used to tional self - identification by the first subject, e.g. , based on improve response over the corresponding controller, which other body signals or by an observer, or a manual classifi lacks the model . cation by third parties using, for example, observation , fMRI [ 0193 ] For example , it is believed that brainwaves repre or psychological assessment. The identified sleep stage is sent a form of resonance , where ensembles of neurons useful, for example, because it represents a target toward ( or, interact in a coordinated fashion . The frequency of the wave in some cases , against ) which the second subject ( recipient) is related to neural responsiveness to neurotransmitters , can be steered . distances along neural pathways, diffusion limitations , etc. [ 0197 ] The stimulation may be one or more stimulus That is , the same sleep stage may be represented by slightly applied to the second subject ( trainee or recipient ), which different frequencies in two different individuals, based on may be a sensory stimulation ( e.g. , visual , auditory, or differences in the size of their brains, neuromodulators tactile ) , mechanical stimulation , ultrasonic stimulation , etc. , present, other anatomical, morphological and physiological and controlled with respect to waveform , frequency, phase , differences , etc. These differences may be measured in intensity / amplitude, duration, or controlled via feedback , microseconds or less , resulting in small changes in fre self- reported effect by the second subject, manual classifi quency . Therefore , the model component of the controller cation by third parties, automated analysis of brain activity , can determine the parameters of neural transmission and behavior, physiological parameters, etc. of the second sub ensemble characteristics, vis - à - vis stimulation , and resyn ject ( recipient ). thesize the stimulus signal to match the correct frequency [ 0198 ] Typically , the goal of the process is to improve and phase of the subjects brainwave , with the optimization sleep in a recipient by transplanting the desired sleep stages , of the waveform adaptively determined . This may not be as or a sequence of stages , of at least one first subject ( donor ) simple as speeding up or slowing down playback of the to the second subject ( recipient) by inducing in the second signal, as different elements of the various brainwaves subject (recipient ) neural correlates of the sleep stage ( or a US 2020/0368491 A1 Nov. 26 , 2020 29 sequence of stages ) of at least one first subject ( donor) configured to modulate the dominant frequency on respec corresponding to the sleep stage of the first subject, through tively the light signal or the sound signal. The stimulus may the use of stimulation parameters comprising a waveform be a light signal , a sonic signal ( sound ), an electric signal, a over a period of time derived from the neural correlates of magnetic field , olfactory or tactile stimulation . The current the sleep stage of the first subject. signal may be a pulse signal or an oscillating signal. The [ 0199 ] Typically, the first and the second subjects are stimulus may be applied via a cranial electric stimulation spatially remote from each other and may be temporally ( CES ) , a transcranial electric stimulation ( TES ) , a deep remote as well . In some cases , the first and second subject electric stimulation , a transcranial magnetic stimulation are the same subject ( human or animal ) , temporally dis ( TMS ) , a deep magnetic stimulation . placed . In other cases , the first and the second subject are [ 0203 ] The technology also provides a processor config spatially proximate to each other. These different embodi ured to process the neural correlates of sleep stage from the ments differ principally in the transfer of the signal from at first subject ( donor) , and to produce or define a stimulation least one first subject ( donor) to the second subject ( recipi pattern for the second subject ( recipient) selectively depen ent) . However, when the first and the second subjects share dent on a waveform pattern of the neural correlates from the a common environment, the signal processing of the neural first subject. Typically, the processor performs signal analy correlates and , especially of real - time feedback of neural sis and calculates at least a dominant frequency of the correlates from the second subject, may involve interactive brainwaves of the first subject, and preferably also spatial algorithms with the neural correlates of the first subject. and phase patterns within the brain of the first subject. The [ 0200 ] According to another embodiment, the first and processor may also perform a PEA , a spatial PEA , an second subjects are each subject to stimulation . In one independent component analysis ( ICA ) , eigenvalue decom particularly interesting embodiment , the first subject and the position , eigenvector -based multivariate analyses, factor second subject communicate with each other in real - time, analysis, an autoencoder neural network with a linear hidden with the first subject receiving stimulation based on the layer, linear discriminant analysis, network component second subject, and the second subject receiving feedback analysis, nonlinear dimensionality reduction (NLDR ), or based on the first subject. This can lead to synchronization another statistical method of data analysis. of neural correlates ( e.g. , neuronal oscillations, or brain [ 0204 ] A signal is presented to a second apparatus, con waves ) and , consequently, of sleep stage between the two figured to stimulate the second subject ( recipient ), which subjects. The neural correlates may be neuronal oscillations may be an open loop stimulation dependent on a non resulting in brainwaves that are detectable as , for example , feedback - controlled algorithm , or a closed loop feedback EEG , QEEG , or MEG signals. Traditionally , these signals are dependent algorithm . The second apparatus produces a found to have dominant frequencies, which may be deter stimulation intended to induce in the second subject (recipi mined by various analyses , such as spectral analysis, wave ent ) the desired sleep stage , e.g. , representing the same sleep let analysis, or principal component analysis ( PCA ) , for stage as was present in the first subject ( donor) . example. One embodiment provides that the modulation [ 0205 ] A typically process performed on the neural cor pattern of a brainwave of at least one first subject ( donor ) is relates is filtering to remove noise . In some embodiments, determined independently of the dominant frequency of the noise filters may be provided , for example, at 50 Hz , 60 Hz , brainwave ( though , typically, within the same class of 100 Hz , 120 Hz , and additional overtones ( e.g. , tertiary and brainwaves ), and this modulation imposed on a brainwave higher harmonics ). The stimulator associated with the sec corresponding to the dominant frequency of the second ond subject ( recipient) would typically perform decoding, subject ( recipient ). That is , once the second subject achieves decompression , decryption , inverse transformation , modu that same brainwave pattern as the first subject ( which may lation , etc. be achieved by means other than electromagnetic , mechani [ 0206 ] Alternately, an authentic wave or hash thereof may cal , or sensory stimulation ), the modulation pattern of the be authenticated via a blockchain , and thus authenticatable first subject is imposed as a way of guiding the sleep stage by an immutable record . In some cases , it is possible to use of the second subject. the stored encrypted signal in its encrypted form , without [ 0201 ] According to another embodiment, the second sub decryption . For example , with an asymmetric encryption ject ( recipient) is stimulated with a stimulation signal, which scheme, which supports distance determination . faithfully represents the frequency composition of a defined [ 0207 ] Due to different brain sizes , and other anatomical, component of the neural correlates of at least one first morphological, and /or physiological differences, dominant subject ( donor ). The defined component may be determined frequencies associated with the same sleep stage may be based on principal component analysis, independent com different in different subjects. Consequently , it may not be ponent analysis ( ICI ) , eigenvector -based multivariable optimal to forcefully impose on the recipient the frequency analysis, factor analysis , canonical correlation analysis of the donor that may or may not precisely correspond to the ( CCA ), nonlinear dimensionality reduction ( NLDR ) , or recipient's frequency associated with the same sleep stage . related technique. Accordingly, in some embodiments, the donors frequency [ 0202 ] The stimulation may be performed , for example, by may be used to start the process of inducing the desired sleep using a light stimulation , a sound stimulation, a tactile stage in a recipient. As some point, when the recipient is stimulation , or olfactory stimulation . An auditory stimulus closed to achieving the desired sleep state , the stimulation is may be , for example , binaural beats or isochronic tones . either stopped or replaced with neurofeedback allowing the Non - sensory stimulation may include a TES device , such as brain of the recipient to find its optimal frequency associated a tDCS device , a high - definition DCS device , an osc - tDCS with the desired sleep stage . device, a pulse - tDCS ( “ electrosleep ” ) device , an osc - tDCS , [ 0208 ] In one embodiment, the feedback signal from the a tACS device , a CES device , a TMS device , rTIMS device , second subject may be correspondingly encoded as per the a deep TMS device, a light source , or a sound source source signal , and the error between the two minimized . US 2020/0368491 A1 Nov. 26 , 2020 30

According to one embodiment, the processor may perform cally or semi- automatically determined or manually entered . a noise reduction distinct from frequency -band filtering . In one embodiment, the records are used to define a modu According to one embodiment, the neural correlates are lation waveform of a synthesized carrier or set of carriers , transformed into a sparse matrix , and in the transform and the process may include a frequency domain multi domain , components having a high probability of represent plexed multi- subcarrier signal ( which is not necessarily ing noise are masked , while components having a high orthogonal ). A plurality of stimuli may be applied concur probability of representing signal are preserved . That is , in rently, through the different subchannels and / or though some cases , the components that represent modulation that different stimulator electrodes, electric current stimulators, are important may not be known a priori. However, depen magnetic field generators, mechanical stimulators, sensory dent on their effect in inducing the desired response in the stimulators, etc. The stimulus may be applied to achieve second subject ( recipient) , the “ important” components may brain entrainment ( i.e. , synchronization ) of the second sub be identified , and the remainder filtered or suppressed . The ject ( recipient) with one or more first subjects ( donors ). If transformed signal may then be inverse - transformed and the plurality of donors are mutually entrained , then each will used as a basis for a stimulation signal. have a corresponding brainwave pattern dependent based on [ 0209 ] According to another embodiment, a method of brainwave entrainment. This link between donors may help sleep stage modification , e.g. , brain entrainment, is pro determine compatibility between a respective donor and the vided , comprising: ascertaining a sleep stage in a plurality of recipient. For example , characteristic patterns in the first subjects ( donors ) ; acquiring brainwaves of the plurality entrained brainwaves may be determined , even for different of first subjects ( donors ) , e.g. , using one of EEG and MEG , target sleep stages , and the characteristic patterns may be to create a dataset containing brainwaves corresponding to correlated to find relatively close matches and to exclude different sleep stages . The database may be encoded with a relatively poor matches . classification of sleep stages , activities , environment, or [ 0214 ] This technology may also provide a basis for a stimulus patterns, applied to the plurality of first subjects, social network , dating site , employment, mission ( e.g. , space and the database may include acquired brainwaves across a or military ), or vocational testing , or other interpersonal large number of sleep stages , activities , environment, or environments, wherein people may be matched with each stimulus patterns, for example . In many cases , the database other based on entrainment characteristics. For example , records will reflect a characteristic or dominant frequency of people who efficiently entrain with each other may have the respective brainwaves. better compatibility and , therefore, a better marriage, work , [ 0210 ] Sleep stages , activities , environment, or stimulus or social relationships than those who do not . The entrain patterns, for example, and a stimulation pattern for a second ment effect need not be limited to sleep stages and may arise subject ( recipient) defined based on the database records of across any context . one or more subjects ( donors ) . [ 0215 ] As discussed above , the plurality of first subjects [ 0211 ] The record ( s ) thus retrieved are used to define a ( donors ) may have their respective brainwave patterns stimulation pattern for the second subject ( recipient ). As a stored in separate database records. However, they may also relatively trivial example, a female recipient could be stimu be combined into a more global model. One such model is lated principally based on records from female donors . a neural network or a deep neural network . Typically, such Similarly, a child recipient of a certain age could be stimu a network would have recurrent features . Data from a lated principally based on the records from children donors plurality of first subjects ( donors ) is used to train the neural of a similar age . Likewise, various demographic , personal network , which is then accessed by inputting the target stage ity, and / or physiological parameters may be matched to and / or feedback information, and which out its a stimula ensure a high degree of correspondence to between the tion pattern or parameters for controlling a stimulator (s ). source and target subjects . In the target subject, a guided or When multiple first subjects ( donors ) form the basis for the genetic algorithm may be employed to select modification stimulation pattern , it is preferred that the neural network parameters from the various components of the signal, output parameters of the stimulation , derived from and which best achieve the desired target state based on feedback comprising features of the brainwave patterns or other from the target subject. neural correlates of sleep stage from the plurality of first [ 0212 ] Of course , a more nuanced approach is to process subject ( donors ), which are then used to control a stimulator the entirety of the database and stimulate the second subject which , for example , generates its own carrier wave ( s ) which based on a global brainwave - stimulus model, though this is are then modulated based on the output of the neural not required , and also , the underlying basis for the model network . A trained neural network need not periodically may prove unreliable or inaccurate . It may be preferred to retrieve records and , therefore , may operate in a more derive a stimulus waveform from only a single first subject time - continuous manner , rather than the more segmented ( donor ), in order to preserve micro -modulation aspects of scheme of record -based control . the signal , which , as discussed above , have not been fully [ 0216 ] In any of the feedback dependent methods, the characterized . However, the selection of the donor ( s ) need brainwave patterns or other neural correlates of sleep stages not be static and can change frequently. The selection of may be processed by a neural network , to produce an output donor records may be based on population statistics of other that guides or controls the stimulation . The stimulation , is , users of the records, i.e. , whether or not the record had the for example, at least one of a light signal , a sound signal, an expected effect, filtering donors whose response pattern electric signal, a magnetic field , an olfactory signal, a correlates highest with a given recipient, etc. The selection chemical signal , and vibration or mechanical stimulus . The of donor records may also be based on feedback patterns process may employ a relational database of sleep stages and from the recipient. brainwave patterns , e.g. , frequencies / neural correlate wave [ 0213 ] The process of stimulation typically seeks to target form patterns associated with the respective sleep stages . the desired sleep stage in the recipient, which is automati The relational database may comprise a first table, the first US 2020/0368491 A1 Nov. 26 , 2020 31 table further comprising a plurality of data records of noise , such as 50 or 60 Hz interference , a frequency trans brainwave patterns, and a second table , the second table form may be performed , followed by a narrow band filtering comprising a plurality of sleep stages , each of the sleep of the interference and its higher order intermodulation stages being linked to at least one brainwave pattern . Data products. An inverse transform may be performed to return related to sleep stages and brainwave patterns associated the data to its time - domain representation for further pro with the sleep stages are stored in the relational database and cessing. ( In the case of simple filtering, a finite impulse maintained . The relational database is accessed by receiving response ( FIR ) or infinite impulse response ( IIR ) filter could queries for selected ( existing or desired ) sleep stages , and be employed ). In other cases , the analysis is continued in the data records are returned representing the associated brain transformed domain . wave pattern . The brainwave pattern retrieved from the [ 0220 ] Transforms may be part of an efficient algorithm to relational database may then be used for modulating a compress data for storage or analysis, by making the rep stimulator seeking to produce an effect selectively depen resentation of the information of interest consume fewer bits dent on the desired sleep stage . of information ( if in digital form ) and / or allow it to be [ 0217 ] A further aspect of the technology provides a communicated using lower bandwidth . Typically, compres computer apparatus for creating and maintaining a relational sion algorithms will not be lossless , and as a result, the database of sleep stages and frequencies associated with the compression is irreversible with respect to truncated infor sleep stage . The computer apparatus may comprise a non mation . volatile memory for storing a relational database of sleep [ 0221 ] Typically, the transformation ( s ) and filtering of the stages and neural correlates of brain activity associated with signal are conducted using traditional computer logic , the sleep stages , the database comprising a first table com according to defined algorithms. The intermediate stages prising a plurality of data records of neural correlates of may be stored and analyzed . However, in some cases , neural brain activity associated with the sleep stages , and a second networks or deep neural networks may be used , convolu table comprising a plurality of sleep stages , each of the sleep tional neural network architectures, or even analog signal stages being linked to one or more records in the first table ; processing. According to one set of embodiments, the trans a processor coupled with the non - volatile memory , and is forms ( if any ) and analysis are implemented in a parallel configured to process relational database queries, which are processing environment. Such as using a SIMD processor then used for searching the database; RAM coupled with the such as a GPU ( or GPGPU ) . Algorithms implemented in processor and the non - volatile memory for temporary hold such systems are characterized by an avoidance of data ing database queries and data records retrieved from the dependent branch instructions, with many threads concur relational database ; and an IO interface configured to receive rently executing the same instructions . database queries and deliver data records retrieved from the [ 0222 ] EEG signals are analyzed to determine the location relational database . A structured query language ( SQL ) or ( e.g. , voxel or brain region ) from which an electrical activity alternate to SQL ( e.g. , noSQL ) database may also be used to pattern is emitted , and the wave pattern characterized . The store and retrieve records . A relational database described spatial processing of the EEG signals will typically precede above , maintained and operated by a general- purpose com the content analysis, since noise and artifacts may be useful puter, improves the operations of the general- purpose com for spatial resolution . Further, the signal from one brain puter by making searches of specific sleep stages and region will typically be noise or interference in the signal brainwaves associated therewith more efficient thereby, lat analysis from another brain region ; so the spatial analysis erals , reducing the demand on computing power . may represent part of the comprehension analysis. The [ 0218 ] A further aspect of the technology provides a spatial analysis is typically in the form of a geometrically method of brain entrainment comprising : ascertaining a and /or anatomically - constrained statistical model, employ sleep stage in at least one first subject ( donor ), recording ing all of the raw inputs in parallel. For example, where the brainwaves of said at least one first subject ( donor) using at input data is transcutaneous electroencephalogram informa least one channel of EEG and / or MEG ; storing the recorded tion , from 32 EEG electrodes, the 32 input channels, brainwaves in a physical memory device, retrieving the sampled at e.g. , 500 sps , 1 ksps or 2 ksps , are processed in brainwaves from the memory device, applying a stimulus a four or higher dimensional matrix , to permit mapping of signal comprising a brainwave pattern derived from at least locations and communication of impulses overtime, space one - channel of the EEG and / or MEG to a second subject and state . ( recipient) via sensory stimulation , whereby the sleep stage [ 0223 ] The matrix processing may be performed in a desired by the second ibject ( recipient) is achieved . The standard computing environment, e.g. , an 19-9900K , stimulation may be of the same dimension ( number of i9-9980HK , processor, under the Windows 10 operating channels ) as the EEG or MEG , or a different number of system , executing Matlab (Mathworks , Woburn Mass . ) soft channels, typically reduced . For example , the EEG or MEG ware platform . Alternately, the matrix processing may be may comprise 64 , 128 , or 256 channels. performed in a computer duster or grid or cloud computing [ 0219 ] One of the advantages of transforming the data is environment. The processing may also employ parallel the ability to select a transform that separates the informa processing , in either a distributed and loosely coupled envi tion of interest represented in the raw data , from noise or ronment, or asynchronous environment. One preferred other information . Some transforms preserve the spatial and embodiment employs a single instruction , multiple data state transition history and may be used for a more global processors , such as a graphics processing unit such as the analysis. Another advantage of a transform is that it can nVidia CUDA environment or AMD Firepro high -perfor present the information of interest in a form where relatively mance computing environment. simple linear or statistical functions of a low order may be [ 0224 ] Artificial intelligence ( AI ) and machine learning applied . In some cases , it is desired to perform an inverse methods, such as artificial neural networks, deep neural transform on the data . For example, if the raw data includes networks , etc., may be implemented to extract the signals of US 2020/0368491 A1 Nov. 26 , 2020 32 interest . Neural networks act as an optimized statistical resulting in the EEG state of the trainer at the time of or classifier and may have arbitrary complexity. A so - called preceding the learning of a skill or a task , or performance of deep neural network having multiple hidden layers may be the task . employed . The processing is typically dependent on labeled [ 0228 ] It is noted that EEG is not the only neural or brain training data , such as EEG data, or various processed , activity or state data that may be acquired , and, of course , transformed , or classified representations of the EEG data . any and all such data may be included within the scope of The label represents the emotion , mood , context, or state of the technology, and therefore EEG is a representative the subject during acquisition . In order to handle the con example only of the types of data that may be used . Other tinuous stream of data represented by the EEG , a recurrent types include fMRI, magnetoencephalogram , motor neuron neural network architecture may be implemented . Depend activity, PET, etc. ing preprocessing before the neural network , formal imple [ 0229 ] While mapping the stimulus - response patterns dis mentations of recurrence may be avoided . A four or more tinct from the task is not required in the trainer, it is dimensional data matrix may be derived from the traditional advantageous to do so , because the trainer may be available spatial - temporal processing of the EEG and fed to a neural for an extended period , the stimulus of the trainee may network . Since the time parameter is represented in the input influence the neural activity patterns, and it is likely that the data , a neural network temporal memory is not required , trainer will have correlated stimulus -response neural activity though this architecture may require a larger number of patterns with the trainee ( s ). It should be noted that the inputs. Principal component analysis ( PCA , en.wikipedia . foregoing has suggested that the trainer is a single indi vidual, while in practice , the trainer may be a population of org / wiki/ Principal_component_analysis ), spatial PCA trainers or skilled individuals . The analysis and processing ( arxiv.org/pdf/1501.03221v3.pdf, adegenetr - forgar -projec of brain activity data may, therefore , be adaptive , both for torg / files/ tutorial- spca.pdf, mmi.ncbi.nlm.nih.gov/pubmed/ each respective individual and for the population as a whole . 1510870 ) ; and clustering analysis may also be employed [ 0230 ] For example, the system may determine that not all ( en.wikipedia.org/wiki/Cluster analysis , see U.S. Pat . Nos . human subjects have common stimulus- response brain 9,336,302 , 9,607,023 and cited references ). activity correlates, and therefore that the population needs to [ 0225 ] In general, a neural network of this type of imple be segregated and clustered . If the differences may be mentation will , in operation , be able to receive unlabeled normalized , then a normalization matrix or other correction EEG data , and produce the output signals representative of may be employed. On the other hand , if the differences do the predicted or estimated task , performance, context, or not permit feasible normalization , the population ( s) may be state of the subject during the acquisition of the unclassified segmented , with different trainers for the different segments . EEG . Of course , statistical classifiers may be used rather For example, in some tasks , male brains have different than neural networks. activity patterns and capabilities than female brains. This , [ 0226 ] The analyzed EEG , either by conventional process coupled with anatomical differences between the sexes , ing , neural network processing, or both , serves two pur implies that the system may provide gender - specific imple poses . First , it permits one to deduce which areas of the brain mentations. Similarly, age differences may provide a rational are subject to which kinds of electrical activity under which and scientific basis for segmentation of the population . conditions . Second , it permits feedback during the training However, depending on the size of the information base and of a trainee ( assuming proper spatial and anatomical corre matrices required , and some other factors , each system may lates between the trainer and trainee ), to help the system be provided with substantially all parameters required for achieve the desired state , or as may be appropriate , the the whole population , with a user - specific implementation desired series of states and / or state transitions . According to based on a user profile or initial setup , calibration , and one aspect of the technology, the applied stimulation is system training session . dependent on a measured starting state or status ( which may [ 0231 ] According to one aspect of the present invention , a represent a complex context and history - dependent matrix of source subject is instrumented with sensors to determine parameters ), and therefore the target represents a desired localized brain activity during experiencing an event . The complex vector change. Therefore , this aspect of the tech objective is to identify regions of the brain involved in nology seeks to understand a complex time - space - brain processing this response . activity associated with an activity or task in a trainer, and [ 0232 ] The sensors will typically seek to determine neuron to seek a corresponding complex time- space - brain activity firing patterns and brain region excitation patterns, which associated with the same activity or task in a trainee , such can be detected by implanted electrodes, transcutaneous that the complex time- space - brain activity state in the trainor electroencephalograms, magnetoencephalograms, fMRI, is distinct from the corresponding state sought to be and other technologies. Where appropriate, transcutaneous achieved in the trainee . This permits the transfer of training EEG is preferred , since this is non - invasive and relatively paradigms from qualitatively different persons , in different simple . contexts , and , to some extent, to achieve a different result. [ 0233 ] The source is observed with the sensors in a quiet [ 0227 ] The conditions of data acquisition from the trainer state , a state in which he or she is experiencing an event, and will include both task data , and sensory - stimulation data . various control states in which the source is at rest or That is , a preferred application of the system is to acquire engaged in different activities resulting in different states . EEG data from a trainer or skilled individual, which will The data may be obtained for a sufficiently long period of then be used to transfer learning, or more likely, learning time and over repeated trials to determine the effect of readiness states , to a naïve trainee. The goal for the trainee duration . The data may also be a population statistical result, is to produce a set of stimulation parameters that will and need not be derived from only a single individual at a achieve , in the trainee , the corresponding neural activity single time. US 2020/0368491 A1 Nov. 26 , 2020 33

[ 0234 ] The sensor data is then processed using a 4D ( or customized configuration to efficiently achieve the informa higher) model to determine the characteristic location -de tion transformations required . Typically, the source and pendent pattern of brain activity over time associated with recipient act asynchronously , with the brain activity of the the state of interest. Where the data is derived from a source recorded and later processed . However, real - time population with various degrees of arousal, the model main processing and brain activity transfer are also possible . In tains this arousal state variable dimension . the case of a general purpose programmable processor [ 0235 ] A recipient is then prepared for receipt of the implementation or portions of the technology , computer mental state . The mental state of the recipient may be instructions may be stored on a nontransient computer assessed . This can include responses to a questionnaire, readable medium . Typically, the system will have special self - assessment, or other psychological assessment methods . purpose components, such as a sensory stimulator, or a Further, the transcutaneous EEG ( or other brain activity modified audio and / or display system , and therefore the data ) of the recipient may be obtained , to determine the system will not be a general purpose system . Further, even starting state for the recipient, as well as an activity during in a general purpose system , the operation per se is enhanced experiencing the desired mental state . according to the present technology . [ 0236 ] In addition , a set of stimuli, such as visual patterns, [ 0241 ] Mental states may be induced in a subject non acoustic patterns, vestibular, smell , taste , touch ( light touch , invasively via light, sound , or other means capable of deep touch , proprioception , stretch, hot , cold , pain , pleasure , conveying frequency patterns. electric stimulation , acupuncture , etc. ), vagus nerve ( e.g. , [ 0242 ] The transmission of the brainwaves can be accom parasympathetic ), are imposed on the subject, optionally plished through direct electrical contact with the electrodes over a range of baseline brain states, to acquire data defining implanted in the brain or remotely employing light, sound, the effect of individual and various combinations of sensory electromagnetic waves , and other non - invasive techniques. stimulation on the brain state of the recipient. Population Light, sound , or electromagnetic fields may be used to data may also be used for this aspect . remotely convey the temporal pattern of prerecorded brain [ 0237 ] The data from the source or population of sources waves to a subject by modulating the encoded temporal ( see above ) may then be processed in conjunction with the frequency on the light, sound or electromagnetic filed signal recipient or population of recipient data , to extract informa to which the subject is exposed . tion defining the optimal sensory stimulation over time of [ 0243 ] Every activity, mental or motor, and emotion is the recipient to achieve the desired brain state resulting in associated with unique brainwaves having specific spatial the desired mental state . and temporal patterns, i.e. , a characteristic frequency or a [ 0238 ] In general, for populations of sources and recipi characteristic distribution of frequencies over time and ents, the data processing task is immense . However, the space . Such waves can be read and recorded by several statistical analysis will generally be of a form that permits known techniques, including electroencephalography parallelization of mathematical transforms for processing ( EEG ) , magnetoencephalography ( MEG ) , exact low - resolu the data, which can be efficiently implemented using various tion brain electromagnetic tomography ( eLORETA ), sen parallel processors , a common form of which is a SIMD sory evoked potentials ( SEP ) , fMRI, functional near- infra ( single instruction , multiple data ) processor, found in typical red spectroscopy ( fNIRS ), etc. The cerebral cortex is graphics processors ( GPUs ) . Because of the cost -efficiency composed of neurons that are interconnected in networks. of GPUs , it is referred to implement the analysis using Cortical neurons constantly send and receive nerve efficient parallelizable algorithms, even if the computational impulses - electrical activity - even during sleep . The electrical complexity is nominally greater than a CISC - type processor or magnetic activity measured by an EEG or MEG ( or implementation another device) device reflects the intrinsic activity of neu [ 0239 ] During stimulation of the recipient, the EEG pat rons in the cerebral cortex and the information sent to it by tern may be monitored to determine if the desired state is subcortical structures and the sense receptors . achieved through the sensory Stimulation . A closed loop [ 0244 ] It has been observed that “ playing back the brain feedback control system may be implemented to modify the waves” to another animal or person by providing decoded stimulation seeking to achieve the target. An evolving temporal pattern through transcranial direct current stimu genetic algorithm may be used to develop a user model , lation (tDCS ), transcranial alternating current stimulation which relates the mental state , arousal, and valence, sensory ( ACS ) , high definition transcranial alternating current stimulation , and brain activity patterns, both to optimize the stimulation (HD - DCS ), transcranial magnetic stimulation current session of stimulation and learning, as well as to ( TMS ) , or through electrodes implanted in the brain allows facilitate future sessions , where the mental states of the the recipient to achieve the mental state at hand or to recipient have further enhanced , and to permit use of the increase the speed of achievement. For example , if the system for a range of mental states . brainwaves of a mouse navigated a familiar maze are [ 0240 ] The technology may be embodied in apparatuses decoded ( by EEG or via implanted electrodes ), playing this for acquiring the brain activity information from the source , temporal pattern to another mouse unfamiliar with this maze processing the brain activity information to reveal a target will allow it to learn to navigate this maze faster. brain activity state and a set of stimuli, which seek to achieve [ 0245 ] Similarly, recording brainwaves associated with a that state in a recipient, and generating stimuli for the specific response of one subject and late “ playing back ” this recipient to achieve and maintain the target brain activity response to another subject will induce a similar response in state over a period of time and potential state transitions . The the second subject. More generally, when one animal generated stimuli may be feedback controlled . A general assumes a mental state, parts of the brain will have charac purpose computer may be used for the processing of the teristic activity patterns. Further, by " artificially ” inducing information , a microprocessor, an FPGA , an ASIC , a sys the same pattern in another animal, the other animal will tem - on - a - chip , or a specialized system , which employs a have the same mental state or more easily be induced into US 2020/0368491 A1 Nov. 26 , 2020 34 that state . The pattern of interest may reside deep in the by a physical arrangement of a stimulator, or natural neural brain , and thus be overwhelmed in an EEG signal by cortical pathways through which the stimulation ( or its result ) potentials and patterns. However, techniques other than passes . surface electrode EEG may be used to determine and [ 0252 ] The EEG pattern may be derived from another spatially discriminate deep brain activity, e.g. , from the individual or individuals , the same individual at a different limbic system . For example , various types of magnetic time , or an in vivo animal model of the desired mental state . sensors may sense deep brain activity . See , e.g. , U.S. Pat . The method may, therefore , replicate a mental state of a first Nos . 9,618,591 ; 9,261,573 ; 8,618,799 ; and 8,593,141 . subject in a second subject. The mental state typically is not [ 0246 ] In some cases , EEGs dominated by cortical exci a state of consciousness or an idea , but rather a subconscious tation patterns may be employed to sense the mental state , ( in a technical sense ) state , representing an emotion , readi since the cortical patterns may correlate with lower - level ness , receptivity, or another state , often independent of brain activity. Note that the determination of a state repre particular thoughts or ideas. In essence , a mental state of the sentation of a mental state need not be performed each time first subject ( a “ trainer ” or “ donor ” who is in a desired the system is used ; rather, once the brain spatial and tem mental state) is captured by recording neural correlates of poral activity patterns and synchronization states associated the mental state , e.g. , as expressed by brain activity patterns, with a particular mental states are determined , those patterns such as EEG or MEG signals. The neural correlates of the may be used for multiple targets and overtime. first subject, either as direct or recorded representations, may [ 0247 ] Similarly, while the goal is , for example, to trigger then be used to control a stimulation of the second subject the target to assume the same brain activity patterns are the ( a “ trainee ” or “ recipient ” ), seeking to induce the same brain exemplar, this can be achieved in various ways , and these activity patterns in the second subject ( recipient /trainee ) as methods of inducing the desired patterns need not be inva were present in the first subject ( donor / trainer ) to assist the sive . Further, user feedback , especially in the case of a second subject ( recipient/ trainee) to attain the desired mental human transferee, may be used to tune the process . Finally, state that had been attained by the donor /trainer . In an using the various senses , especially sight, sound , vestibular, alternative embodiment, the signals from the first subject touch , proprioception , taste , smell , vagus afferent, other (donor /trainer ) being in the first mental state are employed cranial nerve afferent, etc. can be used to trigger high - level to prevent the second subject ( recipient/ trainee ) from mental activity , that in a particular subject achieves the achieving a second mental state , wherein the second mental desired mental state , emotion or mood . state is an undesirable one . [ 0248 ] Thus, in an experimental subject, which may [ 0253 ] The source brain wave pattern may be acquired include laboratory scale and / or invasive monitoring, a set of through multichannel EEG or MEG , from a human in the brain electrical activity patterns that correspond to particular desired brain state . A computational model of the brain state emotions or mental states is determined . Preferably, these is difficult to create . However, such a model is not required are also correlated with surface EEG findings. For the according to the present technology. Rather, the signals may transferee , a stimulation system is provided that is non be processed by a statistical process ( e.g. , PCA or a related hazardous and non - invasive . For example, audiovisual technology ), or a statistically trained process ( e.g. , a neural stimulation may be exclusively used . A set of EEG elec network ). The processed signals preferably retain informa trodes is provided to measure brain activity , and an adaptive tion regarding signal source special location, frequency, and or genetic algorithm scheme is provided to optimize the phase . In stimulating the recipient's brain , the source may be audiovisual presentation, seeking to induce in the transferee modified to account for brain size differences, electrode the target pattern found in the experimental subject. After the locations, etc. Therefore, the preserved characteristics are stimulation patterns, which may be path dependent, are normalized spatial characteristics , frequency, phase , and determined , it is likely that these patterns will be persistent, modulation patterns. though over longer time periods, there may be some desen [ 0254 ] The normalization may be based on feedback from sitization to the stimulation pattern ( s ). In some cases , audio the target subject, for example , based on a comparison of a visual stimulation is insufficient, and TMS , PEMF , or other present state of the target subject and a corresponding state electromagnetic stimulation ( superthreshold , or preferably of the source subject, or another comparison of known states subthreshold ) is employed to assist in achieving the desired between the target and source . Typically, the excitation state and maintaining it for the desired period . electrodes in the target subject do not correspond to the [ 0249 ] Employing light, sound or electromagnetic field to feedback electrodes or the electrodes on the source subject. remotely convey the temporal pattern of brainwaves Therefore, an additional type of normalization is required , may be prerecorded ) to a subject by modulating the encoded which may also be based on a statistical or statistically temporal frequency on the light, sound or electromagnetic trained algorithm . filed signal to which the subject is exposed . [ 0255 ] According to one embodiment, the stimulation of [ 0250 ] When a group of neurons fires simultaneously, the the second subject is associated with a feedback process, to activity appears as a brainwave . Different brainwave - fre verify that the second subject has appropriately responded to quencies are linked to different mental states in the brain . the stimulation , e.g. , has a predefined similarity to the [ 0251 ] The desired mental state may be induced in a target mental state as the first subject, has a mental state with a individual ( e.g. , human , animal ), by providing selective predefined difference from the first subject, or has a desired stimulation according to a temporal pattern , wherein the change from a baseline mental state . Advantageously, the temporal pattern is correlated with an EEG pattern of the stimulation may be adaptive to the feedback . In some cases , target when in the desired mental state , or represents a the feedback may be functional, i.e. , not based on brain transition which represents an intermediate toward achiev activity per se , or neural correlates of mental state , but rather ing the desired mental state . The temporal pattern may be physical, psychological, or behavioral effects that may be targeted to a discrete spatial region within the brain , either reported or observed . US 2020/0368491 A1 Nov. 26 , 2020 35

[ 0256 ] The feedback typically is provided to a computa representations, each corresponding to a technical brain state tional model - based controller for the stimulator, which alters or sequence of sub - states . The sequence may be automati stimulation parameters to optimize the stimulation in depen cally defined , based on biology and the system training, and dence on a brain and brain state model applicable to the thus relieve the programmer of low - level tasks . However, in target. a general case , the present technology maintains the use of [ 0257 ] For example , it is believed that brainwaves repre components or subcomponents of the donors brain activity sent a form of resonance , where ensembles of neurons readings, e.g. , EEG or MEG , and does not seek to charac interact in a coordinated fashion as a set of coupled or terize or abstract them to a semantic level. interacting oscillators. The frequency of the wave is related [ 0260 ] According to the present technology , a neural net to neural responsivity to neurotransmitters, distances along work system or statistical classifier may be employed to neural pathways , diffusion limitations , etc., and perhaps characterize the brain wave activity and / or other data from pacemaker neurons or neural pathways . That is , the same a subject. In addition to the classification or abstraction , a mental state may be represented by different frequencies in reliability parameter is presented , which predicts the accu two different individuals , based on differences in the size of racy of the output. Where the accuracy is high , a model their brains , neuromodulators present, physiological differ based stimulator may be provided to select and / or param ences, etc. These differences may be measured in microsec eterize the model and generate a stimulus for a target subject. onds or less , resulting in fractional changes in frequency . Where the accuracy is low , a filtered representation of the However, if the stimulus is different from the natural or signal may be used to control the stimulator, bypassing the resonant frequency of the target process, the result may be model ( s ). The advantage of this hybrid scheme is that when different from that expected . Therefore , the model - based the model - based stimulator is employed, many different controller can determine the parameters of neural transmis parameters may be explicitly controlled independently of the sion and ensemble characteristics, vis - à - vis stimulation , and source subject. On the other hand , where the data processing resynthesize the stimulus wave to match the correct wave fails to yield a highly useful prediction of the correct form , with the optimization of the waveform adaptively model - based stimulator parameters , the model itself may be determined . This may not be as simple as speeding up or avoided , in favor of a direct stimulation type system . slowing down playback of the signal , as different elements [ 0261 ] Of course , in some cases , one or more components of the various waveforms representing neural correlates of of the stimulation of the target subject may be represented as mental state may have different relative differences between abstract or semantically defined signals, and more generally subjects. Therefore , according to one set of embodiments, the processing of the signals to define the stimulation will the stimulator autocalibrates for the target, based on corre involve high - level modulation or transformation between spondence ( error) of a measured response to the stimulation the source signal received from the first subject, to define the and the desired mental state sought by the stimulation . In target signal for stimulation of the second subject. cases where the results are chaotic or unpredictable based on [ 0262 ] Preferably, each component represents a subset of existing data , a genetic algorithm may be employed to the neural correlates reflecting brain activity that has a high explore the range of stimulation parameters, and determine spatial autocorrelation in space and time , or in a hybrid the response of the target. representation such as wavelet . For example , one signal may [ 0258 ] In some cases , the target has an abnormal or represent a modulated 10.2 Hz signal , while another signal unexpected response to stimulation based on a model main represents a superposed modulated 15.7 Hz signal , with tained within the system . In this case , when the deviance respectively different spatial origins. These may be sepa from the expected response is identified , the system may rated by optimal filtering , once the spatial and temporal seek to a new model , such as from a model repository that characteristics of the signal are known , and bearing in mind may be online , such as through the Internet . If the models are that the signal is accompanied by a modulation pattern , and predictable, a translation may be provided between an that the two components themselves may have some weak applicable model of a source or trainer, and the applicable coupling and interaction. model of the target, to account for differences . In some [ 0263 ] In some cases , the base frequency, modulation , cases , the desired mental state is relatively universal, such as coupling , noise , phase jitter, or another characteristic of the sleep and awake. In this case , the brain response model may signal may be substituted . For example , if the first subject is be a statistical model , rather than a neural network or deep listening to music , there will be significant components of neural network type implementation . the neural correlates that are synchronized with the particu [ 0259 ] Thus, in one embodiment, a hybrid approach is lar music. On the other hand, the music per se may not be provided, with the use of donor -derived brainwaves, on the part of the desired stimulation of the target subject. There one hand , which may be extracted from the brain activity fore , through signal analysis and decomposition , the com readings ( e.g. , EEG or MEG ) of the first at least one subject ponents of the signal from the first subject, which have a ( donor ), preferably processed by principal component analy high temporal correlation with the music, may be extracted sis , or spatial principal component analysis , autocorrelation , or suppressed from the resulting signal . Further, the target or other statistical processing technique ( clustering, PCA , subject may be in a different acoustic environment, and it etc ) or statistically trained technique ( backpropagation of may be appropriate to modify the residual signal dependent errors , etc. ) that separates components of brain activity, on the acoustic environment of the target subject, so that the which can then be modified or modulated based on high stimulation is appropriate for achieving the desired effect, level parameters, e.g. , abstractions . See , ml4a.github.io/ and does not represent phantoms, distractions, or irrelevant m14a / how_neural_networksaretrained /. Thus, the stimula or inappropriate content. In order to perform processing , it tor may be programmed to induce a series of brain states is convenient to store the signals or a partially processed defined by name ( e.g. , sleep stage 1 , sleep stage 2 , etc.) or representation, though a complete real- time signal process as a sequence of “ abstract” semantic labels , icons , or other ing chain may be implemented . Such a real - time signal US 2020/0368491 A1 Nov. 26 , 2020 36 processing chain is generally characterized in that the aver brainwave ( though typically within the same class of brain age size of a buffer remains constant, i.e. , the lag between waves ) , and this modulation imposed on a wave correspond output and input is relatively constant, bearing in mind that ing to the dominant frequency of the second subject. That is , there may be periodicity to the processing. once the second subject achieves that same brainwave [ 0264 ] The mental state of the first subject may be iden pattern as the first subject ( which may be achieved by means tified , and the neural correlates of brain activity captured . other than electromagnetic, mechanical, or sensors stimula The second subject is subject to stimulation based on the tion ) , the modulation pattern of the first subject is imposed captured neural correlates and the identified mental state . as a way of guiding the mental state of the second subject. The mental state may be represented as a semantic variable , [ 0270 ] The second subject may be stimulated with a within a limited classification space . The mental state iden stimulation signal , which faithfully represents the frequency tification need not be through analysis of the neural corre composition of a defined component of the neural correlates lates signal and may be a volitional self - identification by the of the first subject. first subject, a manual classification by third parties, or an [ 0271 ] The stimulation may be performed , for example, by automated determination . The identified mental state is using a source of one of a light signal and a sound signal useful, for example, because it represents a target toward ( or configured to modulate the dominant frequency on the one against ) which the second subject can be steered . of a light signal and a sound signal . The stimulus may be at [ 0265 ] The stimulation may be one or more inputs to the least one of a light signal, a sound signal, an electric signal , second subject, which may be a sensory stimulation , and a magnetic field . The stimulus may be a light stimulation mechanical stimulation , ultrasonic stimulation , etc. , and or a sound stimulation . A visual stimulus may be ambient controlled with respect to waveform , intensity / amplitude , light or direct light. An auditory stimulus may be binaural duration , feedback , self - reported effect by the second sub beats or isochronic tones . ject , manual classification by third parties, automated analy [ 0272 ] The technology may also provide a processor con sis of brain activity, behavior, physiological parameters, etc. figured to process the neural correlates of mental state from of the second subject. the first subject, and to produce or define a stimulation [ 0266 ] The process may be used to induce in the target pattern for the second subject selectively dependent on a subject neural correlates of the desired mental state, which waveform pattern of the neural correlates from the first are derived from a different time for the same person , or a subject. Typically , the processor performs signal analysis different person at the same or a different time . For example , and calculates at least a dominant frequency of the brain one seeks to induce the neural correlates of the first subject waves of the first subject, and preferably also spatial and in a desired mental state in a second subject, through the use phase patterns within the brain of the first subject. of stimulation parameters comprising a waveform over a [ 0273 ] A signal is presented to a second apparatus, con period of time derived from the neural correlates of the figured to stimulate the second subject, which may be an mental state of the first subject. open loop stimulation dependent on a non - feedback con [ 0267 ] The first and second subjects may be spatially trolled algorithm , or a closed loop feedback dependent remote from each other and may be temporally remote as algorithm . In other cases , analog processing is employed in well . In some cases , the first and second subject are the same part or in whole , wherein the algorithm comprises an analog animal ( e.g. , human ), temporally displaced . In other cases , signal processing chain . The second apparatus receives the first and second subject are spatially proximate to each information from the processor ( first apparatus ), typically other. In some cases , neural correlates of a desired mental comprising a representation of a portion of a waveform state are derived from a mammal having a simpler brain , represented in the neural correlates . The second apparatus which are then extrapolated to a human brain . ( Animal brain produces a stimulation intended to induce in the second stimulation is also possible , for example, to enhance training subject the desired mental state , e.g. , representing the same and performance ). When the first and second subjects share mental state as was present in the first subject. a common environment, the signal processing of the neural [ 0274 ] A typical process performed on the neural corre correlates, and especially of real -time feedback of neural lates is filtering to remove noise . For example, notch filters correlates from the second subject may involve interactive may be provided at 50 Hz , 60 Hz , 100 Hz , 120 Hz , and algorithms with the neural correlates of the first subject. additional overtones . Other environmental signals may also [ 0268 ] The first and second subjects may each be subject be filtered in a frequency -selective or waveform - selective to stimulators . The first subject and the second subject may ( temporal) manner . Higher level filtering may also be communicate with each other in real- time, with the first employed , as is known in the art. The neural correlates, after subject receiving stimulation based on the second subject, noise filtering, may be encoded , compressed ( lossy or loss and the second subject receiving feedback based on the first lessly ) , encrypted , or otherwise processed or transformed . subject. This can lead to synchronization of mental state The stimulator associated with the second subject would between the two subjects . However, the first subject need not typically perform decoding, decompression , decryption , receive stimulation based on real - time signals from the inverse transformation , etc. second subject, as the stimulation may derive from a third [ 0275 ] Information security and copy protection technol subject or the first or second subjects at different points in ogy , similar to that employed for audio signals, may be time . employed to protect the neural correlate signals from copy [ 0269 ] The neural correlates may be , for example , EEG , ing or content analysis before use . In some cases , it is QEEG , or MEG signals. Traditionally, these signals are possible to use the stored encrypted signal in its encrypted found to have dominant frequencies, which may be deter for without decryption . For example, with an asymmetric mined by various analyses . One embodiment provides that encryption scheme, which supports distance determination. the modulation pattern of a brainwave of the first subject is See U.S. Pat. No. 7,269,277 ; Sahai and Waters ( 2005 ) determined independent of the dominant frequency of the Annual International Conference on the Theory and Appli US 2020/0368491 A1 Nov. 26 , 2020 37 cations of Cryptographic Techniques, pp . 457-473 . Springer, be accessed according to its indexing, e.g. , mental states, Berlin , Heidelberg; Bringer et al . ( 2009) IEEE International activities , environment, or stimulus patterns, for example, Conference on Communications, pp . 1-6 ; Juels and Sudan and a stimulation pattern for a second subject defined based ( 2006 ) Designs , Codes and Cryptography 2237-257 ; Thaker on the database records of one or more subjects. et al . ( 2006 ) IEEE International Conference on Workload [ 0280 ] The record ( s ) thus retrieved are used to define a Characterization , pp . 142-149 ; Galil et al . ( 1987 ) Confer stimulation pattern for the second subject. The selection of ence on the Theory and Application of Cryptographic Tech records, and their use , may be dependent on the second niques, pp . 135-155 . subject and / or feedback from the second subject. As a [ 0276 ] Because the system may act intrusively, it may be relatively trivial example, a female second subject could be desirable to authenticate the stimulator or parameters stimulated principally dependent on records from female employed by the stimulator before use . For example , the first subjects. Of course , a more nuanced approach is to stimulator and parameters it employs may be authenticated process the entirety of the database and Stimulate the second by a distributed ledger, e.g. , a blockchain . On the other hand , subject based on a global brain wave - stimulus model , in a closed system , digital signatures and other hierarchical though this is not required , and also , the underlying basis for authentication schemes may be employed . Permissions to the model may prove unreliable or inaccurate . It may be perform certain processes may be defined according to smart preferred to derive a stimulus waveform from only a single contracts , which automated permissions ( i.e. , cryptographic first subject, in order to preserve micro - modulation aspects authorization ) provided from a blockchain or distributed of the signal , which as discussed above have not been fully ledger system . Of course , centralized management may also characterized . However, the selection of the first subject ( s ) be employed need not be static and can change frequently. The selection [ 0277 ] In practice, the feedback signal from the second of first subject records may be based on population statistics subject may be correspondingly encoded as per the source of other users of the records ( i.e. , collaborative filtering, i.e. , signal, and the error between the two minimized . In such an whose response pattern do I correlate highest with ? etc. ) . algorithm , the signal sought to be authenticated is typically The selection of first subject records may also be based on brought within an error tolerance of the encrypted signal feedback patterns from the second user. before usable feedback is available . One way to accomplish [ 0281 ] The process of stimulation may seek to target a this is to provide a predetermined range of acceptable desired mental state in the second subject, which is auto authenticatable signals which are then encoded , such that matically or semi- automatically determined or manually authentication occurs when the putative signal matches any entered . That target then represents a part of the query of the predetermined range . In the case of the neural against the database to select the desired record ( s ). The correlates, a large set of digital hash patterns may be selection of records may be a dynamic process , and rese provided representing different signals as hash patterns. The lection of records may be feedback dependent. net result is relatively weakened encryption , but the cryp [ 0282 ] The records may be used to define a modulation tographic strength may still be sufficiently high to abate the waveform of a synthesized carrier or set of carriers , and the risks . process may include a frequency domain multiplexed multi [ 0278 ] The processor may perform a noise reduction dis subcarrier signal ( which is not necessarily orthogonal ). A tinct from frequency - band filtering. The neural correlates plurality of stimuli may be applied concurrently , through the may be transformed into a sparse matrix , and in the trans suffered subchannels and / or through different stimulator form domain , components representing high probability electrodes, magnetic field generators, mechanical stimula noise are masked , while components representing high tors , sensory stimulators , etc. The stimuli for the different probability signal are preserved. The distinction may be subchannels or modalities need not be derived from the optimized or adaptive. That is , in some cases , the compo same records. nents which represent modulation that are important may not [ 0283 ] The stimulus may be applied to achieve the desired be known a priori. However, dependent on their effect in mental state, e.g. , brain entrainment of the second subject inducing the desired response in the second subject, the with one or more first subjects. Brain entrainment is not the “ important ” components may be identified , and the remain only possible outcome of this process . If the plurality of first der filtered or suppressed. The transformed signal may then subjects is mutually entrained , then each will have a corre be inverse - transformed and used as a basis for a stimulation sponding brain wave pattern dependent based on brainwave signal . entrainment. This link between first subject may help deter [ 0279 ] A mental state modification , e.g. , brain entrain mine compatibility between a respective first subject and the ment , may be provided , which ascertains a mental state in a second subject. For example , characteristic patterns in the plurality of first subjects ; acquires brainwaves of the plu entrained brainwaves may be determined , even for different rality of first subjects, e.g. , using one of EEG and MEG , to target mental states , and the characteristic patterns corre create a dataset containing representing brainwaves of the lated to find relatively close matches and to exclude rela plurality of first subjects. The database may be encoded with tively poor matches. a classification of mental state , activities , environment, or [ 0284 ] This technology may also provide a basis for a stimulus patterns, applied to the plurality of first subjects , social network , dating site , employment or vocational test and the database may include acquired brainwaves across a ing , or other interpersonal environments, wherein people large number of mental states , activities , environment, or may be matched with each other based on entrainment stimulus patterns, for example. In many cases , the database characteristics . For example, people who efficiently entrain records will reflect a characteristic or dominant frequency of with each other may have better social relationships than the respective brainwaves . As discussed above, the trainer or those who do not . Thus, rather than seeking to match people first subject is a convenient source of the stimulation param based on personality profiles , the match could be made eters but is not the sole available source . The database may based on the ability of each party to efficiently entrain the US 2020/0368491 A1 Nov. 26 , 2020 38 brainwave pattern of the other party. This enhances non may comprise 128 or 256 channels, while the sensory verbal communication and assists in achieving correspond stimulator may have 8 or fewer channels. Transcranial ing states during activities . This can be assessed by moni stimulation of various modalities and patterns may accom toring neural responses of each individual to video , and also pany the sensory stimulation . by providing a test stimulation based on the other party's [ 0288 ] The present technology may be responsive to chro brainwave correlates of mental state , to see whether cou nobiology, and in particular to the subjective sense of time . pling is efficiently achieved . On the other hand , the tech For a subject, this may be determined volitionally subjec nology could be used to assist in entrainment when the tively , but also automatically, for example by judging atten natural coupling is inefficient or to block coupling where the tion span , using , e.g. , eye movements , and analyzing the coupling is undesirable . An example of the latter is hostility ; persistence of brainwave patterns or other physiological when two people are entrained in a hostile environment, parameters after a discrete stimulus. Further, time - constants emotional escalation ensures . However , if the entrainment is of the brain , reflected by delays and phase may also be attenuated , undesired escalation may be impeded . analyzed . Further, the contingent negative variation (CNV ) [ 0285 ] The process may employ a relational database of preceding a volitional act may be used , both to determine ( or mental states and brainwave patterns, e.g. , frequencies/ measure) conscious action timing , and also the time rela neural correlate waveform patterns associated with the tionships between thought and action more generally. respective mental states . The relational database may com [ 0289 ] Typically, brainwave activity is measured with a prise a first table, the first table further comprising a plurality large number of EEG electrodes, which each receive signals of data records of brainwave patterns , and a second table , the from a small area on the scalp , or in the case of a MEG , by second table comprising a plurality of mental states , each of several sensitive magnetic field detectors, which are respon the mental states being linked to at least one brainwave sive to local field differences . Typically, the brainwave pattern . Data related to mental states and brainwave patterns capture is performed in a relatively high number of spatial associated with the mental states are stored in the relational dimensions , e.g. , corresponding to the number of sensors . It database and maintained . The relational database is accessed is often unfeasible to process the brainwave signals to create by receiving queries for selected mental states , and data a source model , given that the brainwaves are created by records are returned , representing the associated brainwave billions of neurons , connected through axons, which have pattern . The brainwave pattern retrieved from the relational long distances. Further, the neurons are generally non - linear database may then be used for modulating a stimulator and interconnected . However , a source model is not seeking to produce an effect selectively dependent on the required . mental state at issue . [ 0290 ] Various types of artificial intelligence techniques [ 0286 ] A computer apparatus may be provided for creating may be exploited to analyze the neural correlates of a sleep and maintaining a relational database of mental states and stage represented in the brain activity data of both the first frequencies associated with the mental states , the computer subject ( donor ) ( or a plurality of donors ) and the second apparatus comprising: a non - volatile memory for storing a subject ( recipient ). The algorithm or implementation need relational database of mental states and neural correlates of not be the same , though in some cases , it is useful to confirm brain activity associated with the mental states , the database the approach of the source processing and feedback pro comprising a first table , the first table further comprising a cessing so that the feedback does not achieve or seek a plurality of data records of neural correlates of brain activity suboptimal target sleep stage. However, given the possible associated with the mental states , and a second table , the differences in conditions, resources , equipment, and pur second table comprising a plurality of mental states , each of pose , there is no necessary coordination of these processes . the mental states being linked to one or more records in the The artificial intelligence may take the form of neural first table ; a processor coupled with the non - volatile networks or deep neural networks, though rule / expert -based memory, configured to process relational database queries, systems , hybrids, and more classical statistical analysis may which are then used for searching the database ; RAM be used . In a typical case , an artificial intelligence process coupled with the processor and the non - volatile memory for will have at least one aspect , which is non - linear in its output temporary holding database queries and data records response to an input signal , and thus at least the principle of retrieved from the relational database ; and an I / O interface linear superposition is violated . Such systems tend to permit configured to receive database queries and deliver data discrimination , since a decision and the process of decision records retrieved from the relational database . An SQL or making are, ultimately, non -linear . An artificially intelligent nos database may also be used to store and retrieve system requires a base of experience or information upon records . which to train . This can be supervised ( external labels [0287 ] A further aspect of the technology provides a applied to data ), unsupervised ( self -discrimination of method of brain entrainment comprising : ascertaining a classes ) , or semi- supervised ( a portion of the data is exter mental state in a first subject; recording brainwaves of the nally labeled ). plurality of subjects using at least one channel one of EEG [ 0291 ] A self - learning or genetic algorithm may be used to and MEG ; storing the recorded brainwaves in a physical tune the system , including both or either the signal process memory device ; retrieving the brainwaves from the memory ing at the donor system and the recipient system . In a genetic device ; applying a stimulus signal comprising a brainwave algorithm feedback - dependent self - learning system , the pattern derived from at least one - channel one of the EEG responsivity of a subject, e.g. , the target, to various kinds of and MEG to a second subject via sensory stimulation , stimuli may be determined over a stimulus space . This whereby the mental state desired by the second subject is stimulation may be in the context of use , with a specific achieved . The stimulation may be of the same order ( number target sleep stage provided , or unconstrained . The stimulator of channels) as the EEG or MEG , or a different number of may operate using a library of stimulus patterns, or seek to channels, typically reduced . For example, the EEG or MEG generate synthetic patterns or modifications of patterns. US 2020/0368491 A1 Nov. 26 , 2020 39

Over some time , the system will learn to map the desired [ 0295 ] In a medical treatment implementation, in some sleep stage to optimal context -dependent parameters of the cases it may be appropriate to administer a drug or phar stimulus pattern . macological agent, such as melatonin , hypnotic or soporific [ 0292 ] The technology may be used for both the creation drug, a sedative ( e.g. , barbiturates, benzodiazepines, non of a desired sleep stages in the recipient, elimination of benzodiazepine hypnotics , orexin antagonists, antihista existing sleep stages in the recipient. In the latter case , a mines , general anesthetics, cannabis and other herbal seda decision of what end state is to be achieved is less con tives , methaqualone and analogues , muscle relaxants , strained , and therefore , the optimization is distinct . For opioids ) that assists in achieving the target sleep stage , and example, in the former case , it may be hard to achieve a for emotional states and / or dreams, this may include certain particular sleep stage that is desired , requiring a set of psychotropic drugs, such as epinephrine, norepinephrine transitions to cause the brain of the recipient to be enabled / reuptake inhibitors, serotonin reuptake inhibitors , peptide prepared to enter the target state . In the case of a system endocrine hormones, such as oxytocin , ACTH fragments , seeking to eliminate an undesired sleep stage , the issue is insulin , etc. Combining a drug with stimulation may reduce principally what path to take to most efficiently leave the the required dose of the drug and the associated side effects current state , bearing in mind the various costs , such as the of the drug comfort / discomfort of the stimulation , the time value cost , [ 0296 ] It is an object to provide a method of brain entrain etc. Therefore, the series of states may differ in the imple ment to facilitate sleep in a subject using a sleep app mentation of these distinct goals , even if the endpoints are executing on a user device , the method comprising: execut identical, i.e. , the optimal algorithm to achieve state B from ing the sleep app on the user device , configured to select at state A , may be different from the optimal algorithm to exist least one stimulus selected from the group consisting of at state A , and end up at state B. least one of a light signal and a sound signal; selecting a [ 0293 ] The technology may be used to address sleep waveform from a menu having a plurality of waveforms stages or sections of them associated with dreaming. Typi derived from brainwaves of at least one sleeping donor, cally, dreaming is associated with many different brain wherein said waveform corresponds to at least one specific regions . As such , the biology of dreaming is different. Often , stage of sleep; and stimulating the subject with said at least dreams have a biochemical or hormonal component and , one stimulus, wherein said at least one stimulus is modulated perhaps, a physiological component, that may be attenuated with the selected waveform , to thereby entrain the brain of or absent from cognitive states. Dreaming had long been the subject with the selected waveform to facilitate sleep in thought to occur largely during rapid - eye -movement ( REM ) the subject. The user device may be , e.g. , a mobile device, sleep , but dreams have also been reported to occur during a wearable device , or an implantable device. The stimulus non - REM sleep . However, dreams are typically remem may be a sound signal, comprising at least one of a prede bered , if the dreamer wakes us during the REM phase of the termined soundtrack , a tone , and white noise . The sound sleep . In addition, it has been shown that dreaming, for signal may comprise a soundtrack representing at least one example , about faces was linked to increased high - fre of a sound of rainfall, a sound of a waterfall, a sound of quency activity in the specific region of the brain involved ocean waves , a lullaby , a melody, and a polyphony. in face recognition , with dreams involving spatial percep tion , movement and thinking similarly linked to regions of [ 0297 ] The effect of the stimulus may be monitored by the brain that handle such tasks when awake. Therefore , feedback , e.g. , EEG , body temperature, heart rate , respira while the general brainwave or other neural correlates tion rate, facial expression , muscle tone , vasodilation , which acquisition from a donor may be similar or identical, the may be measured by non - contact sensors or wearable stimulus used on the second subject ( recipient) may be devices, and other electronic sensors embedded in the bed , distinct in modality , spatial location , intensity /waveform , blanket, mattress , sheets , pillow , etc. Body movement and other stimulation parameters , and the types and application eye movement may be observed by a video camera or of feedback employed . webcam . The sensor signals are advantageously transmitted [ 0294 ] It is known that people who have more REM sleep back to the user device to adjust the regime of stimulation . and more intense theta ( 4 Hz - 7 Hz ) activity during REM are Of course , the communication path may be indirect to the better able to consolidate emotional memories . It was sug user device , or the analysis of the signals may be remote gested (Blagrove ) that if we attempt to hack our dreams by from the user device , e.g. , in a cloud computing center . An artificially increasing theta waves , it might lead to the important aspect of the system is synchronizing the cycles incorporation of more waking experiences into our dreams. with the context and current state of the subject. For ( See “ Dreams act as overnight therapy ” New Scientist example , if the subject got up to go to the bathroom or woke magazine on 5 May 2018 ) . Transplanting theta frequency up for other reasons, the modulation cycle would generally brainwaves from a vivid dreamer may also help achieve the need to restart from sleep stage 1. However, depending on same effect. Moreover, instead of stimulating the subjects the mental state of the subject, the progression through the brain with a synthetic theta frequency ( e.g. , isotonic tones or sleep states may be varied . ambient sound beats ) , stimulating the recipient's brain using [ 0298 ] The sound may be amplitude modulated on a donors brainwaves carrying secondary ( and higher) harmon carrier waveform , which would generally have higher fre ics , in addition to the dominant theta frequency, may induce quencies that the modulation waveform ( typically < 100 Hz ) , the same category of dreams, i.e. , if the donor dreamed of and / or frequency modulated . When the sound separation people , the recipient will be more likely to dream of people , between ears is present, the amplitude, frequency, phase , albeit different people , because the donors brainwaves will timing , etc. between ears may be modulated . Similarly, stimulate the visual cortex of the recipient. This may be optical signals may be modulated by intensity, color, fre helpful in the treatment of MD , stress management, phobias quency, phase , etc. , in addition to morphological objects and and some psychiatric diseases. dynamic changes in real time . US 2020/0368491 A1 Nov. 26 , 2020 40

[ 0299 ] The at least one waveform may be derived from an stimulation of the subject in real time according to a bio EEG recordings of brainwaves of at least one sleeping feedback loop implemented by the user device . donor, processed using at least one of a principal component [ 0308 ] It is also an object to provide a mobile device, analysis ( PCA ) , a correspondence analysis ( CA ) , a factor comprising a housing ; a microprocessor disposed within the analysis , a K -means clustering , a non -negative matrix fac housing ; and a non - volatile memory disposed within the torization ( NMF ) , a sparse PEA , a non - linear PCA a robust housing and electrically coupled with the processor , config PCA , an independent component analysis ( ICA ) , a network ured to store at least one app for controlling the micropro component analysis, and a singular spectral analysis. cessor ; the at least one app being configured to : ( a ) select a [ 0300 ] Gender of the subject may be determined , and the waveform from a plurality of waveforms derived from gender of the subject marched with a gender of said at least brainwaves of at least one sleeping donor, wherein said one sleeping donor. waveform corresponds to at least one specific stage of sleep ; [ 0301 ] The at least one specific stage of sleep may be one and ( b ) stimulate a subject with said at least one stimulus , of stage 1 of sleep , stage of sleep , stage 3 of sleep , and wherein at least one stimulus selected from the group stage 4 of sleep . In some analyses , 4 different non - REM consisting of at least one of an auditory stimulus and a visual ( NREM ) stages are classified , with stages 3 and 4 being deep stimulus is modulated with the selected waveform , to sleep stages . See , nu.sleepassociation.org/about-sleep/ thereby entrain the brain of the subject with the selected stages -of -sleep /, FIG . 17. At least one specific stage of sleep waveform to facilitate sleep in the subject. The mobile may be one of REM sleep , non - REM sleep , and slow - wave device may further comprise a battery electrically coupled sleep . At least one specific stage of sleep may be at least one with the processor , a display, disposed within the housing , complete sleep cycle comprising a natural sequence of sleep electrically coupled with the microprocessor, a wireless stages from stage 1 through stage 4 ( REM ) . The at least one communication transceiver disposed within the housing , complete sleep cycle may comprise least three sequential electrically coupled with the microprocessor, at least one complete sleep cycles . microphone , electrically coupled with the processor, at least [ 0302 ] The user device may comprise at least one speaker one speaker disposed within the housing , electrically and wherein the stimulus comprises a sound signal delivered coupled with the processor , and at least one camera electri through said at least one speaker, and comprises an iso cally coupled with the processor. The mobile device may be chronic tone . The sound signal may be delivered to the wirelessly coupled with a wearable device , wherein said subject through a pair of wireless earbuds, e.g. , the modu wearable device comprises at least one biometric sensor lated selected waveform may comprise binaural beats . configured to communicate biometric data from the subject [ 0303 ] The user device may be configured to control an to the mobile device through the wireless communication ambient light, which is selectively controllable to change at transceiver . The housing may be wearable by the subject least one of brightness and color, and wherein the stimulus and / or maintained close to the skull of the subject with a comprises a light signal which is presented to the subject headband . through the ambient light. The light signal may be generated [ 0309 ] Another object provides a method of brain entrain by at least one light emitting diode ( LED ) . The LED may be ment to facilitate sleep in a subject using a sleep app , disposed in proximity to the subjects eyes , e.g. , in a sleep comprising opening the sleep app on a programmable mask . device ; choosing at least one stimulus, wherein said at least [ 0304 ] The user device may comprise at least one biomet one stimulus is one of a light signal and a sound signal ; ric sensor , further comprising the step of monitoring and choosing a waveform from a menu having a plurality of collecting biometric data of the subject from said at least one waveforms derived from brainwaves of at least one sleeping biometric sensor. donor, wherein said waveform corresponds to at least one [ 0305 ] The method may further comprise monitoring specific stage of sleep ; and stimulating the subjects brain movement of the subject using at least one of a camera in the with said at least one stimulus , wherein said at least one user device and a webcam coupled with the user device , stimulus is modulated with the chosen waveform to entrain processed with a neural network configured to classify a the brain of the subject with frequencies of the brainwaves subjects sleep as one of a REM sleep , non - REM sleep , and of the at least one sleeping donor, to facilitate sleep in the a slow -wave sleep ; and adjusting the stimulating of the subject. The method may further comprise recording a subject upon determining whether the classification. subjects electroencephalogram ( EEG ) during sleep while [ 0306 ] The method may further comprise monitoring a stimulated ; and adjusting the stimulating based on the sub facial expression of the subject to determine if the subject is jects electroencephalogram ( EEG ) in real time using a asleep or awake, and controlling a sequence of sleep stages neurofeedback loop . induced by said stimulating in dependence on at least the [ 0310 ] A further object provides a method ofbrain entrain monitored facial expression . The stimulating may be con ment to facilitate sleep in a subject, comprising: providing a trolled to progress according to a natural series of sleep programmable device having a sleep app stored in a non stages , further comprising resetting the progress according volatile memory ; providing a stimulator, selected from one to the natural series of sleep stages in dependence on an or more of a light stimulator and a sound stimulator, defining awakening of the subject determined based on the monitored a waveform , by the sleep app , from a plurality of wave facial expression . The facial expression may be monitored forms, each respective waveform being derived from brain by at least one of a camera in the user device and a webcam waves of at least one sleeping donor, wherein said waveform communicating with the user device . The facial expression corresponds to at least one specific stage of sleep ; stimulat may be monitored according to a signal present in at least ing the subject with the stimulator, having at least one of a one electromyographic electrode. light output or a sound output modulated with the defined [ 0307 ] The method may further comprise obtaining bio waveform , to entrain the brain of the subject with the feedback from the subject in real time and adjusting the brainwaves of the at least one sleeping donor, to facilitate US 2020/0368491 A1 Nov. 26 , 2020 41 sleep in the subject. The method may further comprise brain activity patterns of the donor in the recipient. The recording an electroencephalogram ( EEG ) from the subject method may further comprise verifying that the recipient is during sleep while being stimulated , and defining at least asleep . one new waveform for stimulation , said waveform being [ 0316 ] It is still a further object to provide a method of selectively dependent on the uploaded recorded electroen preventing sleep in a second subject ( recipient) comprising : cephalogram . The at least one new waveform may be used identifying a mental state of a first subject ( donor ); if the to stimulate the subject one day after the electroencephalo donor is awake, recording brain activity patterns of the first gram is recorded . The recorded electroencephalogram may subject; and preventing sleep in the second subject by be uploaded to a remote server , and the new waveform for replicating the brain activity patterns of the second subject. stimulation subsequently downloaded from the remote The method may further comprise verifying that the second server . subject is awake . [ 0317 ] Another object is a method of transplanting a [ 0311 ] Another object provides a non -transitory computer desired mental state from a first subject ( donor ) to a second readable medium storing instructions for controlling a pro subject ( recipient) comprising: identifying a mental state of cessor to perform a method comprising: instructions to the donor, capturing a mental state of the donor by recording select a waveform from a plurality of waveforms derived brain activity patterns; saving the brain activity patterns in a from brainwaves of at least one sleeping donor, wherein said non - volatile memory ; retrieving the brain activity patterns waveform corresponds to at least one specific stage of sleep ; from the non - volatile memory ; and transplanting the desired and instructions to stimulate a subject with said at least one mental state of the donor to the recipient by inducing the stimulus , wherein at least one stimulus selected from the brain activity patterns in the recipient, wherein the desired group consisting of at least one of an auditory stimulus and mental state is one a sleeping state and a waking state . a visual stimulus is modulated with the selected waveform , [ 0318 ] Another object is a method of transplanting a to thereby entrain the brain of the subject with the selected desired sleep stage from a first subject ( donor ) to a second waveform to facilitate sleep in the subject. subject ( recipient) comprising: identifying a sleep stage of [ 0312 ] A still further object provides a method of gener the donor, capturing a sleep stage of the donor by recording ating a waveform for neuromodulation to improve sleep in brain activity patterns; saving the brain activity patterns in a a subject, the method comprising: collecting EEG recording non - volatile memory ; retrieving the brain activity patterns from at least one sleeping donor ; identifying portions of the from the non - volatile memory ; and transplanting the desired EEG recordings corresponding to a specific sleep stage ; sleep stage of the donor to the recipient by inducing the brain grouping a plurality of portions of the EEG recordings activity patterns in the recipient, wherein the desired sleep corresponding to the specific sleep stage , each group corre stage is one a sleep stage 1 , 2 , and 3 . sponding to the specific sleep stage ; analyzing each group [ 0319 ] Another object is a method of transplanting a corresponding to the specific sleep stage using a statistical desired sleep stage from a first subject ( donor ) to a second analysis ; extracting a cortical signature corresponding to subject ( recipient) comprising: identifying a sleep stage of each specific sleep stage ; generating a waveform based on the donor, capturing a sleep stage of the donor by recording the cortical signature for each sleep stage ; and modulating a brain activity patterns; saving the brain activity patterns in a stimulus for the subject according to the waveform . The non - volatile memory ; retrieving the brain activity patterns modulating of the stimulus may be performed under control from the non - volatile memory ; and transplanting the desired of a sleep app executing on a mobile or wearable device . The sleep stage of the donor to the recipient by inducing the brain statistical analysis may be at least one of a principal com activity patterns in the recipient, wherein the desired sleep ponent analysis ( PCA ) , a correspondence analysis ( CA ) , a stage is one of a REM sleep stage and non - REM sleep stage . factor analysis , a K - means clustering, a non - negative matrix [ 0320 ] Another object is a method of transplanting a factorization (NMF ) , a sparse PEA a non - linear PCA a desired sleep stage from a first subject ( donor ) to a second robust PEA an independent component analysis ( ICA ) , a subject ( recipient) comprising: identifying a sleep stage of network component analysis , and a singular spectral analy the donor, capturing a sleep stage of the donor by recording sis . brain activity patterns ; saving the brain activity patterns in a [ 0313 ] It is , therefore , an object to provide a method of non - volatile memory ; retrieving the brain activity patterns inducing sleep in a second subject comprising : recording from the non - volatile memory ; and transplanting the desired brain activity patterns of a first subject ( donor ) who is sleep stage of the donor to the recipient by inducing the brain asleep ; and inducing sleep in the second subject ( recipient) activity patterns in the recipient, wherein the desired sleep by replicating the brain activity patterns of the donor in the stage is a slow - wave deep non - REM sleep . recipient. [ 0321 ] A further object is a method of improving sleep in a recipient by transplanting a mental state of a donor to the [ 0314 ] It is also an object to provide a method of prevent recipient comprising: recording brainwaves of the donor, ing sleep in a second subject ( recipient) comprising : record and transplanting the mental state of the donor to the ing brain activity patterns of a first subject ( donor ) who is recipient by inducing the recorded brainwaves of the donor awake ; and preventing sleep in the second subject ( recipient ) in the recipient, wherein the mental state is one of a waking by replicating the brain activity patterns of the donor in the state and a sleeping state . recipient. [ 0322 ] A still further object is a method of transplanting a [ 0315 ] It is further an object to provide a method of desired mental state of a first subject (donor ) to a second inducing sleep in a second subject ( recipient) comprising: subject comprising: identifying a mental state of the donor, identifying the mental state of a first subject ( donor ); if the recording brainwaves of the donor in a desired mental state ; donor is asleep , recording brain activity patterns of the and transplanting the desired mental state of the donor to the donor, and inducing sleep in the recipient by replicating the recipient by inducing the brainwaves of the first subject in US 2020/0368491 A1 Nov. 26 , 2020 42 the second subject, wherein the desired mental state is one the brainwaves in the recipient to transplant to the recipient of a sleeping state and a waking state . the desired mental state of the donor, the second apparatus [ 0323 ] Another object is a method of improving sleep in a configured to receive said at least one dominant frequency of recipient by transplanting the desired state of a healthy sleep the brainwaves of the donor from the non - volatile memory , to the recipient comprising: identifying a mental state of the wherein the desired mental state is one of a sleeping state plurality of healthy donors recording brainwaves of the and a waking state . plurality of healthy donor in a state of sleep ; saving the [ 0327 ] The second apparatus may be a light source brainwaves in a non - volatile memory ; retrieving the brain capable of modulating said at least one dominant frequency waves from the non - volatile memory ; and transplanting the on the light, a sound source capable of modulating said at state of healthy sleep from the plurality of healthy donors to least one dominant frequency on the sound , or a combination the recipient by inducing the brainwaves of the donor in the thereof. The sound source may be binaural beats source or recipient. The method may comprise identifying a mental isochronic tones source . state of the recipient to verify that the recipient has the desired mental state . The brainwaves may be recorded using [ 0328 ] A further object is a method of transplanting a EEG , QEEG , or MEG . The method may further comprise circadian rhythm of a first subject ( donor) to a second filtering the recorded brainwaves from noise and / or perform subject ( recipient) comprising: recording EEG or MEG of ing PCA to determine dominant frequencies and secondary the donor, the donor having a desirable phase of the circa ( and , possibly , higher) harmonics. dian rhythm ; processing the recorded EEG or MEG to [ 0324 ] A further object is a system for transplanting a remove noise ; saving the processed EEG or MEG in a desired mental state from a first subject ( donor ) to a second nonvolatile memory ; retrieving the processed EEG or MEG subject (recipient ) comprising: a first apparatus for recording from the nonvolatile memory ; and transplanting the desired brainwaves of the donor in a desired mental state ; a non phase of the circadian rhythm of the donor to the recipient volatile memory coupled with the first apparatus for storing by “ playing back ” the processed EEG or MEG of the donor the recording of the brainwaves; and a second apparatus for to the recipient via sensory stimulation or other one or more inducing the brainwaves in the recipient to transplant to the stimulus on which the donors EEG or MEG is modulated . recipient the desired mental state of the donor, the second The method may further comprise compressing the recorded apparatus configured to receive the recording of the brain EEG or MEG , before saving it in the non - volatile memory ; waves of the donor from the non - volatile memory , wherein and decompressing the recorded EEG or MEG after retriev the desired mental state is one of sleeping state and a ing compressed EEG or MEG from the non -volatile waking state . The first apparatus may be one of an electro memory encephalograph and a magnetoencephalograph. The second [ 0329 ] Yet another object is a system for transplanting a apparatus may be one of a source of light signal or sound circadian rhythm of a first subject ( donor) to a second signal configured to modulate donors brainwave frequencies subject ( recipient ) comprising: an electroencephalograph or on the light signal or the sound signal. a magnetoencephalograph for recording EEG or MEG [ 0325 ] Another object is a method of transplanting a respectively ; a first processor coupled to the electroencepha desired mental state of a first subject ( donor) to a second lograph or the magnetoencephalograph and configured for subject (recipient ) comprising : identifying a mental state of digital signal processing for removing noise from the the donor, recording at least one of EEG and MEG of the recorded EEG or MEG ; a non - volatile memory coupled with donor, said donor being in a desired mental state ; processing the processor for storing the processed EEG or MEG ; and a the EEG or MEG signal; saving the processed signal in a stimulation device coupled to the non - volatile memory for nonvolatile memory ; retrieving the processed signal from playing back the processed EEG or MEG to the recipient to the nonvolatile memory ; modulating the processed signal on induce the circadian rhythm of the donor to the recipient. at least one stimulus; and transplanting the desired mental The stimulation device may be a sensory stimulation device , state of the first subject to the second subject by stimulating a source of light or a source of the sound , each capable of the second subject with said at least one stimulus, wherein modulating recorded EEG or MEG on a light signal or a the desired mental state is a sleeping state or a waking state . sound signal respectively . The first processor may be further Theprocessingmay compriseremoving noise fromthe EEG configured to compress the processed EEG or MEG . A or MEG signal ; and / or compressing the EEG or MEG signal . second processor configured to decompress compressed The EEG or MEG signal retrieved from the nonvolatile EEG or MEG may be coupled to the non - volatile memory memory may be decompressed . The stimulus may be a light and to the stimulation device . signal, a sound signal , or a combination thereof. The light [ 0330 ] The technology may be used to modify or alter a stimulation may be an ambient light or a direct light. The mental state ( e.g. , from sleep to waking and vice versa ) in a sound stimulation may be binaural beats or isochronic tones . subject. Typically, the staging mental state, brain state , or [ 0326 ] A still another object is a system for transplanting brainwave pattern is assessed , such as by EEG , MEG , a desired mental state of a first subject ( donor) to a second observation , stimulus - response amplitude and / or delay, or subject ( recipient) comprising: an electroencephalograph or the like . Of particular interest in uncontrolled environments a magnetoencephalograph for recoding brainwaves of the are automated mental state assessments , which do not rely donor, the donor being in a desired mental state ; a processor on human observation or EEG signals , and rather may be coupled with an electroencephalograph or a magnetoen acquired through MEG ( e.g. , SQID , optically -pumped mag cephalograph , the processor configured to perform signal netometer ), EMG , MMG (magnetomyogram ), mechanical analysis and calculate at leastone dominant frequency ofthe ( e.g. , accelerometer, gyroscope, etc. ) , data from physiologi brainwaves of the donor, a nonvolatile memory coupled with cal sensors ( e.g. , EKG , heartrate, respiration rate , tempera the first processor for storing the at least one frequency of ture , galvanic skim potential, etc. ) , or automated camera the brainwaves of the donor, a second apparatus for inducing sensors US 2020/0368491 A1 Nov. 26 , 2020 43

[ 0331 ] For example, cortical stimulus - response pathways patterns can reflect a time delay between stimulation and and reflexes may be exercised automatically, to determine motor response , an amplitude of motor response , distribu their characteristics on a generally continuous basis . These tion of response through various afferent pathways, the characteristics may include , for example, a delay between variability of response , tremor or other modulation of motor stimulus and the observed central ( e.g. , EEG ) or peripheral activity , etc. Combinations of these characteristics may be response ( e.g. , EMG , limb accelerometer, video ) . Typically, employed , and different subsets may be employed at differ the same modality will be used to assess the pre - stimulation ent times or to reflect different states . Similar to evoked state , stimulus response, and post- stimulation state , though potentials , the stimulus may be any sense , especially sight , this is not a limitation . sound , touch / proprioception / pain / etc ., though the other [ 0332 ] In order to change the mental state , a stimulus is senses, such as taste , smell , balance , etc. , may also be applied in a way designed to alter the mental state in the exercised . A direct electrical or magnetic excitation is also desired manner. A state transition table, or algorithm , may be possible . As discussed , the response may be determined employed to optimize the transition from a starting mental through EEG , MEG , or peripheral afferent pathways. state to a desired mental state . The stimulus may be provided [ 0337 ] A further object provides a system and method for in an open loop (predetermined stimulus protocol ) or closed enhancing deep non - REM deep , comprising statistically loop ( feedback adapted stimulus protocol ), based on separating slow -wave sleep components from acquired observed changes in a monitored variable . brainwave patterns; defining a stimulation pattern based on [ 0333 ] Advantageously, a characteristic delay between the statistically separating slow - wave sleep components, and application of stimulus and determination of response varies stimulating a subject with the defined stimulation pattern . with the brain or mental state . For example , some mental The neurological stimulator comprises a memory configured states may lead to an increased delay or greater variability in to store acquired brainwave patterns; at least one processor delay, while others may lead to decreased or lower variabil configured to : statistically separate slow -wave non -REM ity . Further, some states may lead to attenuation of response , sleep components from the acquired brainwave patterns ; and while others may lead to an exaggerated response . In addi define a brain stimulation pattern based on the statistically tion , different mental states can be associated with qualita separating slow -wave non - REM deep sleep components ; tively different responses. Typically, the mere assessment of and an output signal generator configured to defined brain the brain or mental state should not itself alter the state , stimulation pattern . though in some cases the assessment and transition influence [ 0338 ] A still further object provides a system and method may be combined . For example, in seeking to assist in for enhancing deep sleep , comprising : extracting brainwave achieving a deep sleep state , the excitation that disturbs patterns representing a deep sleep state comprising slow sleep is contraindicated . wave sleep , from indigenous brain activity EEG recordings [ 0334 ] In cases where a brainwave pattern is itself deter of at least one subject; processing the extracted brainwave mined by EEG ( which may be limited to relatively con patterns using a statistical processing algorithm to separate trolled environments ), brainwaves representing that pattern slow wave sleep components from the indigenous brain represent coherent firing of an ensemble of neurons, defining activity EEG recordings of the at least one subject; inverting a phase . One way to change the state is to advance or retard the processed extracted brainwave patterns; and stimulating the triggering of the neuronal excitation, which can be a a subject with the inverted processed extracted brainwave director indirect excitation or inhibition , caused , for patterns. The corresponding system for enhancing deep example, by electrical, magnetic mechanical, or sensory sleep comprises a memory configured to store brainwave stimulation . This stimulation may be time- synchronized patterns representing a deep sleep state comprising slow with the detected ( e.g. , by EEG ) brainwaves, for example wave sleep , from indigenous brain activity EEG recordings with a phase lead or lag with respect to the detected pattern . of at least one subject at least one processor configured to Further , the excitation can steer the brainwave signal by process the extracted brainwave patterns using a statistical continually advancing to the desired state , which through the processing algorithm to separate slow wave sleep compo continual phase rotation represents a different frequency . nents from the indigenous brain activity EEG recordings of After the desired new state is achieved , the stimulus may the at least one subject; and a stimulator, configured to cease , or be maintained in a phase - locked manner to hold the generate a stimulation signal based on the processed desired state . extracted brainwave patterns . The stimulator may comprise [ 0335 ] A predictive model may be used to determine the a sensory stimulator ( e.g. , sight, sound, vestibular, touch , current mental state , optimal transition to a desired mental taste , smell , etc. ). In order to format the signal for stimu state , when the subject has achieved the desired mental state , lating the brain , it may be inverted . Normalization of brain and how to maintain the desired mental state . The desired activity information may be spatial and / or temporal. mental state itself may represent a dynamic sequence ( e.g. , [ 0339 ] See also US 2016/0066838 ( DeCharms) ; US 2009 / stage 1- > stage 2 - stage 3 , etc. ) , such that the subjects 0099623 ( Bentwich ); US 201210289869 A1 ( Tyler ); US mental state is held for the desired period in a defined 2004/0131998 (Marmon et al . ) ; U.S. Pat . No. 5,356,368 condition. Accordingly, the stimulus may be time -synchro (Monroe ); US 2002/0198577 ( Jaillet ); and US 2015 / nized with respect to the measured brainwave pattern . 0291074 ( Advanced Telecommunications Research Institute [ 0336 ] Direct measurement or determination of brain International ). DeCharms discloses a computer - assisted waves or their phase relationships is not necessarily method for treating pain in a subject comprising measuring required . Rather , the system may determine tremor or reflex activity of one or more internal voxels of a brain of said patterns. Typically, the reflex patterns of interest involve subject associated with pain ; communicating instructions to central pathways, and more preferably brain reflex path said subject which modulate the activity of said voxel , and ways , and not spinal cord mediated reflexes, which are less training said subject to control said internal voxel . DeC dependent on instantaneous brain state . The central reflex harms provides methods, software, and systems that may be US 2020/0368491 A1 Nov. 26 , 2020 44 used to provide and enhance the activation and control of sleep in the subject; wherein at least one of the selection of one or more regions of interest, particularly through training the waveform and the definition of the stilulus is responsive and exercising those regions of interest. Data analysis / to the at least one microphone or the at least one camera . behavioral control software performs computations of brain [ 0342 ] It is another object to provide a method of facili scan image data to produce activity metrics that are mea tating sleep , comprising: providing data defining a plurality sures of physiological activity in brain regions of interest. of waveforms in a memory ; retrieving a selected waveform The results and other information and ongoing collected data from the memory , selectively dependent on at least one of a may be stored to data files of progress and a record of the determined sleep phase of a human subject and a predeter stimuli used . The selected instruction , measured informa mined sequence ; and stimulating the human subject with a tion , or stimulus, is then presented via a display to a subject. stimulus modulated according to the selected waveform ; to This encourages the subject to engage in imagined or thereby entrain the brain of the human subject with the performed behaviors or exercises or to perceive stimuli . If selected waveform to facilitate sleep in the subject. the subject undertakes overt behaviors, such as responding [ 0343 ] The plurality of waveforms in the memory may be to questions, the responses and other behavioral measure derived from brain activity measurements acquired during at ments are fed to the data analysis / behavioral control soft least one sleep cycle of at least one human , or from brain ware . According to DeCharms, a subject can be trained to activity measurements acquired during at least one sleep control the activation of a region of interest of that subject's cycle of the human subject. brain , and then exercise the use of that region to further [ 0344 ] The method may further comprise acquiring brain increase the strength and control of its activation . This activity measurements during at least one sleep cycle of at training and exercise can have beneficial effects on the least one human ; and processing the acquired brain activity subject. In the case of regions that release endogenous measurements to define the plurality of waveforms in the neuromodulatory agents, this control can serve a role similar memory . to that of externally applied drugs . [ 0345 ] The stimulus may be modulated in a human subject [ 0340 ] It is also an object to provide a method of gener device, according to a sleep app stored within the human ating a waveform for neuromodulation to improve sleep in subject device, the sleep app being downloadable and a subject, the method comprising: collecting EEG record upgradeable from a remote server . ings from at least one sleeping donor for a plurality of sleep [ 0346 ] The predetermined sequence may be defined by a stages ; grouping a plurality of portions of the EEG record human user interface menu of a human subject device for ings corresponding to the plurality of sleep stages, into a selecting at least one respective waveform . plurality of groups corresponding to the plurality of sleep [ 0347 ] The sleep phase of the human subject may be stages ; analyzing each group using a statistical analysis ; determined based on at least electroencephalographic activ extracting a cortical signature corresponding characteristic ity of the human subject or based on at least bioelectric of each analyzed group ; generating a waveform based on the signals received from the human subject. characteristic comical signature for each of the plurality of [ 0348 ] The stimulus modulated according to the selected sleep stages , and modulating a stimulus for the subject waveform may entrain the brain of the human subject with according to the generated waveforms for the plurality of the selected waveform to facilitate sleep in the human sleep stages. subject . [ 0341 ] It is a further object to provide a mobile device contained within a housing , comprising: a microprocessor ; [ 0349 ] The stimulus modulated according to the selected an electrical power source, electrically coupled with the waveform may be one of alight stimulus and a sound microprocessor ; a wireless communication transceiver, elec stimulus . trically coupled with the microprocessor, at least one micro [ 0350 ] The selected waveform may correspond to at least phone port, electrically coupled with the microprocessor, one specific stage of sleep . configured to receive an electrical signal corresponding to a [ 0351 ] Each of the plurality of waveforms may be derived sound ; at least one camera port electrically coupled with the from recordings of brainwaves of at least one sleeping microprocessor, configured to receive an electrical signal donor, processed using a statistical decision analysis. corresponding to an image ; a display , electrically coupled [ 0352 ] The method may further comprise adaptively with the microprocessor, at least one speaker port, electri defining a sequence of sleep stages dependent on biometric cally coupled with the microprocessor, configured to gen information received from a sleeping human subject; and erate an electrical signal corresponding to a sound ; a non selecting waveforms from the memory in dependence on a volatile memory and electrically coupled with the correspondence to a respective sleep stage of the adaptively microprocessor, configured to store at least one app down defined sequence of sleep stages ; wherein said stimulating loadable through the wireless communication transceiver for the human subject comprises altering a sleep stage of the controlling the microprocessor, said at least one download human subject dependent on both the determined sleep able app being configured to : ( a ) select a waveform from a phase of a human subject and the adaptively defined plurality of waveforms derived from brainwaves of at least sequence of sleep stages . one sleeping donor, wherein said waveform corresponds to [ 0353 ] The human subject may be stimulated with at least at least one a specific stage of sleep , a gender, and an age one audio transducer and wherein the stimulus comprises at group ; and ( b ) define a stimulus for stimulation of a subject, least one of an isochronic tone and binaural beats or with an selected from the group consisting of at least one of an ambient light stimulus, selectively modulated according to auditory stimulus generated through the at least one speaker, the selected waveform to change at least one of brightness and a visual stimulus generated through the display, modu and color . The ambient light stimulus may be emitted by at lated with the selected waveform , and adapted to entrain the least one light emitting diode disposed in a sleep mask brain of the subject with the selected waveform to facilitate proximate the human subjects eyes . US 2020/0368491 A1 Nov. 26 , 2020 45

[ 0354 ] The method may further comprise providing at [ 0368 ] FIG . 12 shows a flowchart according to an embodi least one sensor to determine at least one of an eye move ment of the invention . ment and a facial expression of the human subject, to at least [ 0369 ] FIG . 13 shows a flowchart according to an embodi one of determine a current determined sleep phase of a ment of the invention . human subject or select the predetermined sequence . [ 0370 ] FIG . 14 shows a flowchart according to an embodi [ 0355 ] The predetermined sequence may be a natural ment of the invention . series of sleep stages , the method further comprising reset [ 0371 ] FIG . 15 shows a flowchart according to an embodi ting the progress according to the natural series of sleep ment of the invention . stages in dependence on an awakening of the human subject. [ 0372 ] FIG . 16 shows a schematic representation of a smartphone for executing apps . BRIEF DESCRIPTION OF THE DRAWINGS [ 0373 ] FIG . 17 shows a hypnogram of a healthy adult . [ 0374 ] FIG . 18 shows a hypnogram of a healthy adult . [ 0356 ] The detailed description is described with refer [ 0375 ] FIG . 19 shows a sequence of sleep stages in a ence to the accompanying figures . In the figures , the left healthy adult . most digit ( s ) of a reference number identifies the figure in [ 0376 ] FIG . 20A shows an original EEG recording of a which the reference number first appears. The use of the REM phase in a 34 years old female. same reference number in different figures indicates similar [ 0377 ] FIG . 20B shows an EEG recording of a REM phase or identical items. in a 34 years old female reconstructed with 64 SSA groups. [ 0357 ] FIG . 1 shows a flowchart according to one embodi [ 0378 ] FIG . 20C shows an EEG recording of a REM phase ment of the invention illustrating a process of replicating a in a 34 years old female reconstructed with 16 SSA groups . sleep state from one subject to another subject. [ 0379 ] FIGS . 21A and 21B show an EEG for a 30 years [ 0358 ] FIG . 2 shows a flowchart according to one embodi old female in sleep stage R. ment of the invention illustrating a process of replicating a [ 0380 ] FIG . 22 show an EEG for a 30 years old female in waking stage from one subject to another subject by record sleep stage 3 . ing and replicating brainwaves associated with the waking [ 0381 ] FIGS . 23A and 23B show an EEG for a 30 years stage , according to one embodiment of the invention . old female in sleep stage 3 . [ 0359 ] FIG . 3 shows a flowchart according to one embodi [ 0382 ] FIGS . 24A and 24B show an EEG for a 25 years ment of the invention illustrating a process of replicating a old female in sleep stage W. sleep stage from at least one first subject to another subject [ 0383 ] FIGS . 25A and 25B show an EEG for a 25 years by recording electroencephalogram ( EEG ) of said least one old male in sleep stage 2 . first subject, extracting at least one dominant frequency from [ 0384 ] FIGS . 26A and 26B show an EEG for a 25 years the EEG and replicating the sleep stage of said at least one old male in sleep stage 1 . first subject in a second subject by stimulating the second [ 0385 ) FIGS . 27A and 27B show an EEG for a 25 years subject with stimuli having the dominant frequency associ old male in sleep stage W. ated with the desired sleep stage , according to one embodi [ 0386 ] FIG . 28 shows a schematic diagram of a mental ment of the invention . state transfer system . [ 0360 ] FIG . 4 shows a flowchart according to one embodi ment of the invention illustrating a method of improving DETAILED DESCRIPTION OF THE sleep in a recipient by recording EEG or MEG of a healthy PREFERRED EMBODIMENTS donor and “ playing it back ” to the recipient via transcranial [ 0387 ] Hereinafter, embodiments the present disclosure stimulation . will be described in detail with reference to the accompa [ 0361 ] FIG . 5 shows a flowchart according to one embodi nying drawings so that the present disclosure may be readily ment of the invention illustrating the creation of a database implemented by those skilled in the art. However, it is to be of sleep stages and their associated frequencies for later noted that the present disclosure is not limited to the brain entrainment. embodiments but can be embodied in various other ways. In [ 0362 ] FIG . 6 shows a flowchart according to one embodi drawings , parts irrelevant to the description are omitted for ment of the invention illustrating using a neural network in the simplicity of explanation , and like reference numerals the creation of a database of sleep stages and their associated denote like parts through the whole document. frequencies for later brain entrainment . [ 0388 ] FIG . 1 shows a flowchart of a first embodiment [ 0363 ] FIG . 7 shows a flowchart according to one embodi according to the present invention . A first subject ( donor ), ment of the invention illustrating a method of recording a having a mental state , is interrogated , observed or sensed , to mental state of a first subject in a desirable state of the determine or identify his or her mental state 100. The first subject's circadian rhythm and transplanting this mental subject is typically human , though this is not a limit of the state into another subject to replicated the desirable state of technology and the subject may be an animal . In this the circadian rhythm . embodiment, the process seeks to identify a characteristic [ 0364 ] FIG . 8 shows a flowchart according to a further sleep pattern , and therefore the mental state of the first embodiment of the invention . subject is monitored until a sleep state occurs 110. When the [ 0365 ] FIG.9 shows a flowchart according to one embodi first subject ( donor ) is asleep , brain activity patterns reflect ment of the invention illustrating a process of replicating the ing or characterizing the sleep state are captured 120. This desired sleep stage from one subject to another subject. step may be done by recording EEG or MEG of the first [ 0366 ] FIG . 10 shows a flowchart according to an embodi subject ( donor ). And the brain activity patterns are stored in ment of the invention . a non - volatile memory 130. These stored patterns may be [ 0367 ] FIG . 11 shows a flowchart according to an embodi optionally processed , statistically aggregated , analyzed for ment of the invention . perturbations or anomalies , filtered , compressed , etc. Stages US 2020/0368491 A1 Nov. 26 , 2020 46 of sleep may be determined . It is noted that brain activity ( donor ) , having a mental state , is interrogated , observed or patterns change over time during sleep from stage to stage , sensed, to determine or identify of his or her mental state and therefore , the stored patterns may encompass one or 100. The first subject is typically human , though this is not more stages of sleep . a limit of the invention ( which equally applies to any [ 0389 ] The stored data from the first subject ( donor ) is animal ). In this embodiment, the interrogation seeks to then used to induce sleep in a second subject ( a recipient identify a characteristic alert / awake pattern , and therefore also typically a human , but may be an animal) by replicating the mental state of the first subject is monitored until an alert the brain activity patterns ( or sequences of brain activity state occurs 111. When the first subject ( donor) is awake, patterns) of the first subject ( donor ) in the second subject brain activity patterns reflecting or characterizing the wak ( recipient) 150. The replication of brain activity patterns, ing state are captured 120 , and stored in a non - volatile dependent on the stored patterns, typically seeks to stimulate memory 130. For example, one may seek to capture the or induce the brain of the second subject ( recipient) by patterns that represent awakening, and therefore , the moni modulating a stimulus ( or several stimuli ) in a manner toring commences on a sleeping subject. These stored pat synchronized with the frequency, phase and / or waveform terns may be optionally processed , statistically aggregated , pattern represented in the brain activity patterns of the first analyzed for perturbations or anomalies , filtered , com subject ( donor ) in the sleep state . Typically, when the second pressed , etc Stages of awakening may be determined . It is subject (recipient ) achieves the sleep state 160 (assuming noted that the brain activity patterns change over time during that the first subject and second subject are physiologically awakening, and therefore, the stored patterns may encom compatiblea donor and a recipient should both be either pass one or more stages of the waking process . human or animals ), the brain activity patterns of the first and [ 0395 ] The stored data from the first subject ( donor) is second subject will be corresponding. then retrieved from the non - volatile memory 140 and used [ 0390 ] According to the present technology, the modula to “ transplant ” the state of alertness to prevent sleep , or tion of stimulation , which is , for example, a sensory stimu maintain alertness, in a second subject ( a recipient also lation , whose waveform is modulated to correspond to the typically, but not necessarily, a human ) by replicating the raw or processed brainwave pattern of the first subject awake brain activity patterns of the first subject ( donor ), or ( donor ) for the brain region associated with the stimulation sequences of brain activity patterns, in the second subject electrode . ( recipient) 170. The replication of brain activity patterns , [ 0391 ] For example, the brain activity pattern of the first dependent on the stored patterns, typically seeks to stimulate subject ( donor ) is measured by EEG electrodes . In a sleep or induce the brain of the second subject ( recipient) by state , it may assume various wave patterns, over the range modulating indigenous brainwaves of the donor on a stimu < 1 Hz to about 25 Hz , which vary in amplitude, frequency, lus in a manner synchronized with the frequency, and spatial location , and relative phase. For example , the first preferably phase and / or waveform pattern represented in the stage of sleep is initially dominated by alpha brainwaves brain activity patterns of the first subject ( donor ) in the with a frequency of 8 Hz to 13 Hz . Typically , brain activity awake or wakening state . Typically, when the second subject pattern measurement from the first subject ( donor) has a is awake or wakes up , 180 , the brain activity patterns of the higher spatial resolution , e.g. , 64 or 128 electrode EEGS , first and second subject will be corresponding. than the stimulator for the second subject ( recipient ), and the stimulus electrodes tend to be larger than the EEG electrode . [ 0396 ] FIG . 3 shows a flowchart of a third embodiment, in The stimulus for the second subject ( recipient) is therefore which the technology is generalized . A first subject ( donor ), processed using a dimensionality ( or spatial) reduction algo having a mental state , is interrogated , observed or sensed , to rithm to account for these differences , which will tend to determine or identify his or her mental state 190. The mental filter the stimulus signal. By applying this stimulus modu state of the first subject is monitored until the desired state lated with the brain activity of the first subject ( donor ), the is achieved 200. When the first subject achieves that state , second subject ( recipient) is made susceptible to synchro brain activity patterns reflecting or characterizing the state nization with the brain activity pattern of the first subject are captured 210 by, for example, recording EEG or MEG of ( donor ). For example, by temporally modulating the polar the first subject, and optionally stored in non - volatile ization level of the cells near the electrode, the cells will memory . The brain activity pattern is , e.g. , brainwaves ( e.g. , better couple to excitation stimuli in the brain of the second EEG) 210 . subject ( recipient) having the characteristics of the brain [ 0397 ] The brainwaves are analyzed using statistical data activity pattern of the first subject ( donor ) . mining techniques such as principal component analysis [ 0392 ] The donors indigenous brainwaves may be modu ( PCA ) to determine a set of linearly -uncorrelated vari lated on light, sound, vibrations or any number of other ables — principal components . At least one dominant fre stimuli amenable to frequency modulation . For example, quency in the recorded brainwaves is identified 220. Option donors brainwaves may be modulated on ambient light, on ally , secondary and higher harmonics may be identified as binaural beats , or isochronic tones . well . It will be well - understood by a person skilled in the ad [ 0393 ] The verification that the recipient has achieved the that any number of similar statistical data analysis tech desired sleep state may optionally be done by visual obser niques may be used , such as signal processing , independent vation , by EEG , EKG , measuring heart and / or respiration component analysis, network component analysis, corre rate , body temperature or any number of other physiological spondence analysis , multiple correspondence analysis, fac parameters that will be well understood by a person skilled tor analysis, canonical correlation , functional principal com in the art . These measurements should be , preferably, done ponent analysis, independent component analysis, singular automatically via biosensors. spectrum analysis, weighted PEA , sparse PCA principal [ 0394 ] FIG . 2 shows a flowchart of the second embodi geodesic analysis , eigenvector - based multivariate analyses , ment according to the present invention . A first subject etc. US 2020/0368491 A1 Nov. 26 , 2020 47

[ 0398 ] The stored data from the first subject is then subject or hasten the transition from one state to another . It retrieved , at least the dominant frequency is modulated on at may also be used to treat circadian rhythm disorders, by least one stimulus and used to “ transplant ” the desired reinforcing healthy or normal circadian rhythm patterns in a mental state of the donor in a second subject ( recipient) by second subject with an otherwise abnormal cycle . seeking to replicate the brain activity patterns of the first [ 0403 ] FIG . 8 shows a flowchart according to a further subject ( donor ) , or sequences of brain activity patterns, in embodiment of the present invention illustrating a process of the second subject ( recipient) 240. The second subject replicating the desired sleep stage from one subject ( donor ) ( recipient) is then monitored for induction of the desired to another subject ( recipient ). In general, the sleep stage of mental state 250 . the source subject is determined in a traditional manner, [ 0399 ] FIG . 4 shows a flowchart according to the fourth which may include brain signal analysis, other biometrics, embodiment, in which an EEG or EMG of a first subject and / or observation . The data may be acquired 400 over one ( healthy donor ), while in a state of sleep , is recorded 260 , or more sleep cycles , and during or after different types of optionally processed to remove noise 270 , and stored 280 . environmental conditions or stimulation . For example , vari The data may optionally be compressed. The stored data is ous types of music may be played , seeking to entrain a retrieved 290 and decompressed as necessary . The data is conscious or subconscious rhythm . Lights can flash , and then played back to a second subject ( recipient ), to improve various other sensory stimulation may occur. The brain the quality of sleep 300 . signal readings are synchronized and tagged with the stimu [ 0400 ] FIG . 5 shows a flowchart according to the fifth lation parameters 410 so that the stimulation is associated embodiment, in which a multichannel EEG /EMG of a first with its respective effect . Similarly, before sleep , the subject subject ( donor ) is recorded 310 , and processed to remove may be presented with certain experiences , such that during noise ( and / or artifacts ) and / or compress the data 320. It is sleep , the memory processing within the brain is dependent optionally stored in non - volatile memory. PCA analysis is on these experiences . performed on the data to determine characteristic frequen [ 0404 ] After the various data is acquired from the subject cies associated with sleep stages 330. A database is created , 400 , along with information about the pre - sleep experience storing the recorded EEG /MEG , the associated characteris and or context 410 , and sensory Stimulation during sleep , a tic frequencies, and corresponding sleep stages , so that a memory , database , statistical model , the rule - based model is characteristic frequency may be retrieved for any given generated , and / or neural network is trained , reflecting the sleep stage 340. This database can be a relational database subject ( donor ). Data may be aggregated from a plurality of or any other type of searchable database as will be readily subjects ( donors ), but typically, these are processed for the understood by anyone skilled in the art . According to the particular subject before aggregation . Based on single or sixth embodiment, a multichannel EEG / EMG of a first multiple subject data, a normalization process may occur subject ( donor) is recorded 310 , and processed to remove 420. The normalization may be spatial and / or temporal. For noise ( and / or artifacts ) and / or compress the data 320. It is example, the EEG electrodes between sessions or for the optionally stored in non - volatile memory . An artificial neu different subject may be in different locations, leading to a ral network is trained on this data to determine characteristic distortion of the multichannel spatial arrangement. Further, frequencies associated with sleep stages 350. A deep neural the head size and shape of different individuals are different, network , as well as other Al machine learning tools , may be and this needs to be normalized and /or encoded as well . The used as will be readily understood by a person skilled in the size and shape of the head / skull and / or brain may also lead art. A database is created, storing the recording of the to temporal differences in the signals, such as characteristic EEG /MEG , the associated characteristic frequencies, and time delays , resonant or characteristic frequencies , etc. corresponding sleep stages , so that a characteristic fre [ 0405 ] One way to account for these effects is through the quency may be retrieved for any given sleep stage 340 . use of a time - space transform , such as a wavelet - type [ 0401 ] FIG . 6 shows a flowchart according to an embodi transform . It is noted that, in a corresponding way that ment of the invention . A multichannel EEG or EMG of a statistical processes are subject to frequency decomposition plurality of healthy sleeping donors is recorded 310. The analysis through Fourier transforms, they are also subject to multichannel EEG / EMG recordings are processed too , e.g. , time - frequency decomposition through wavelet transforms. remove noise 320. A neural network is trained on the Typically, the wavelet transform is a discrete wavelet trans recorded EEG / EMG recordings to identify characteristic form ( DINT ) , though more complex and less regular trans frequencies associated with sleep stages 350. A database of forms may be employed. As discussed above , principal sleep stage characteristic frequencies is created . component analysis ( PCA ) and spatial PCA may be used to [ 0402 ] FIG . 7 shows a flowchart according to a further analyze signals , presuming linearity ( linear superposition ) embodiment of the present invention illustrating a process in and statistical independence of components. However, these which a first subject ( donor) is monitored with respect to presumptions technically do not apply to brainwave data , phases of his or her circadian rhythm with his or her EEG or and practically , one would normally expect interaction EMG recorded 360 , processed to remove noise ( and / or between brain wave components ( non - independence ) and artifacts ), and , optionally , compressed 270 , and then stored lack of linearity ( since “ neural networks ” by their nature are in a non - volatile memory 280. In this case , the stored signals non - linear ), defeating the use of PCA or spatial PCA are tagged with the circadian cycle phase , unless only a unmodified. However, a field of nonlinear dimensionality single phase is captured , or pattern recognition used to reduction provides various techniques to permit correspond identify the cycle stage . The stored data is then retrieved ing analyses under the presumptions of non - linearity and 290 , decompressed 370 , and played back to a second subject non - independence . See , en.wikipeda.org/wiki/Nonlinear_ ( recipient) 380 , using sensory stimulation, or other stimuli, dimensionality_reduction , www.image.ucar.edu/pub/toyN/ to induce a desired circadian rhythm state . In this case , the monahan_5_16.pdf ( An Introduction to Nonlinear Principal technology may also be used to prolong states in the second Component Analysis, Adam Monahan ), Nonlinear PCA US 2020/0368491 A1 Nov. 26 , 2020 48 toolbox for MATLAB (www.nlpca.org ), Nonlinear PCA or MEG data ” ( 2014 ) ; Friston , Karl J. “ Basic concepts and ( www.comp.nus.edu.sg/cs5240/lecture/nonlinear-pca.pdf ), overview .” SPMcourse , Short course ; Crainiceanu , Ciprian Nonlinear Principal Components Analysis: Introduction and M. , Ana - Maria Staicu , Shubankar Ray, and Naresh Punjabi. Application ( openaccess.leidenuniv.nl/bitstream/handle/ “ Statistical inference on the difference in the means of two 1887 /12386 / Chapter2.pdf ? sequence = 10 , 2007 ) , Nonlinear correlated functional processes : an application to sleep EEG Principal Component Analysis : Neural Network Models and power spectra . ” Johns Hopkins University, Dept. of Biosta Applications (pdfs.semanticscholar.org/9d31/ tistics Working Papers ( 2011 ) : 225 ; Konar, Amit , and Aruna 23542031a227d2f4c4602066cf8ebceaeb7a.pdf ), Karl Fris Chakraborty. Emotion recognition : A pattern analysis ton , “ Nonlinear PCA : characterizing interactions between approach . John Wiley & Sons , 2014 ; Kohl , Florian . “ Blind modes of brain activity ” (www.fil.ion.uctac.uk/karl/Nonlin separation of dependent source signals for MEG sensory earPCA.pdf, 2000 ), Howard et al . , “ Distinct Variation Pat stimulation experiments .” ( 2013 ) ; Onken , Arno , Jian K Liu , tern Discovery Using Alternating Nonlinear Principal Com PP Chamanthi R. Karunasekara, loannis Delis , Tim ponent Analysis ” , IEEE Trans Neural Network Learn Syst Gollisch , and Stefano Panzeri. “ Using matrix and tensor 2018 January; 29 ( 1 ) : 156-166 . doi : 10.1109 / TNNLS.2016 . factorizations for the single - trial analysis of population spike 2616145. Epub 2016 Oct. 26 (www.ncbi.nlm.nih.gov/ trains. ” PLoS computational biology 12 , no . 11 ( 2016 ) : pubmed / 27810837 ); Jolliffe, I. T. , “ Principal Component e1005189 ; Tressold , Patrizio , Luciano Pederzoli, Marco Analysis, Second Edition ” , Springer 2002 , cda.psych.uiuc . Bilucaglia , Patrizio Caini , Pasquale Fedele , Alessandro Fer edu/ statistical_learning course / Jolliffe I. Principal Compo rini, Simone Melloni , Diana Richeld , Florentina Richeld , nent Analysis ( 2ed . , Springer, 2002 ) (518s ) _MVsa_.pdf, and Agostino Accardo . “ Brain - to - Brain ( Mind - to - Mind ) Stone , James V. “ Blind source separation using temporal Interaction at Distance : A Confirmatory Study . ” ( 2014 ) . predictability . " Neural computation 13 , no . 7 (2001 ) : 1559 f1000researchdata . s3.amazonaws.com/manuscripts/5914/ 1574 ; Barros , Allan Kardec , and Andrzej Cichocki . “ Extrac 5adbf847-787a - 4fc1 - ac04-2elcd61ca972_4336 _ - _ patrizio tion of specific signals with temporal structure . " Neural tressoldi_v3.pdf ? doi = 10.12688 / f1000research.4336.3 ; Tsia computation 13 , no . 9 ( 2001 ) : 1995-2003 ; Lee , Soo - Young. pa ras , Nikolaos N. “ Wavelet analysis in coherence estima “ Blind source separation and independent component analy tion of electroencephalographic signals in children for the sis : A review . ” Neural Information Processing -Letters and detection of dyslexia - related abnormalities . ” PhD diss . , Reviews 6 , no . 1 ( 2005 ) : 1-57 ; Hyvärinen , Aapo , and Patrik 2006 . Hoyer. “ Emergence of phase- and shift - invariant features by [ 0406 ] FIG . 9 shows a flowchart of an embodiment of the decomposition of natural images into independent feature invention . Asleep stage of a first subject is identified , and subspaces. ” Neural computation 12 , no . 7 ( 2000 ) : 1705 then it is determined whether the sleep stage is the desired 1720 ; Wahlund , Björn , Wlodzimierz Klonowski , Pawel sleep stage . If not, the first subject is further monitored . If the Stepien , Robert Stepien, Tatjana von Rosen , and Dietrich sleep stage is the one desired , the brain activity of the first von Rosen . “ EEG data , fractal dimension and multivariate subject is captured, reflecting the sleep stage , and the brain statistics. ” Journal of Computer Science and Engineering 3 , activity patterns of the first subject while in the desired sleep no . 1 ( 2010 ) : 10-14 ; Yu , Xianchuan , Dan Hu , and Jindong stage stored in non - volatile memory 500. The stored brain Xu . Blind source separation : theory and applications. John activity patterns are subsequently retrieved and used to Wiley & Sons , 2013 ; Panda, Shantipriya, Satchidananda induce the sleep stage in a second subject by replicating the Dehuri , and Sung - Bae Cho . “ Machine Learning Approaches brain activity patterns of the first subject in the second for Cognitive State Classification and Brain Activity Pre subject by appropriate stimulation of the second subject. The diction : A Survey . " Current Bioinformatics 10 , no . 4 ( 2015 ) : second subject may be monitored to verify that the second 344-359 ; Friston , Karl J. , Andrew P. Holmes , Keith J. subject is in the desired sleep stage . Worsley, J - P . Poline , Chris D. Frith , and Richard S J Frack [ 0407 ] As shown in FIG . 10 , a human brain state or mental owiak . “ Statistical parametric maps in functional imaging : a state in a subject is modified or altered . In some implemen general linear approach .” Human brain mapping 2 , no . 4 tations , a current brainwave pattern of the subject, a phase of ( 1994 ) : 189-210 ; Wang , Ya n , Matthew T. Sutherland, Lori L a characteristic wave of the current brainwave pattern of the Sanfratello , and Akaysha C. Tang. “ Single - trial classifica subject, a characteristic timing of a stimulus- response tion of ERPS using second - order blind identification dependent on the mental state , or temporal relationships in ( SOBI) . ” In Machine Learning and Cybernetics, 2004. Pro monitored neurological or motor patterns of the subject is ceedings of 2004 International Conference on , vol . 7 , pp . determined . The desired change in the current brain wave 4246-4251 . IEEE , 2004 ; Jutten , Christian , and Massoud pattern of the subject is determined or defined . A stimulus is Babaie - Zadeh . “ Source separation: Principles, current applied , e.g. , electrical, magnetic, acoustic or ultrasound, advances and applications . ” IAR Annu Meet Nancy Fr 110 sensory, etc. , which can be for determining the current state , ( 2006 ) ; Saproo , Sameer, Victor Shih , David C Jangraw , and changing the state , or both . For example, a characteristic Paul Sajda . “ Neural mechanisms underlying catastrophic timing of a stimulus - response dependent on the mental state failure in human -machine interaction during aerial naviga may be extracted , or temporal relationships in monitored tion . ” Journal of neural engineering 13 , no . 6 ( 2016 ) : neurological or motor patterns of the subject determined . 066005 ; Valente, Giancarlo . “ Separazione cieca di sorgenti The stimulus may be asynchronous, or time - synchronized in ambienti reali : nuovi algoritmi, applicazionie implemen with respect to the phase state , or dependent on at least the tazioni. ” ( 2006 ) ; SAPIENZAL A. “ Blind Source Separation determined temporal relationships. In a closed - loop excita in real -world environments: new algorithms, applications tion , the brain wave pattern of the subject after at least one and implementations Separazione cieca di sorgenti in ambi stimulus is monitored or the response parameters, e.g. , enti reali: nuovi algoritmi, applicazionie . ” ; Ewald , Arne . characteristic timing measured or assessed . The stimulus “ Novel multivariate data analysis techniques to determine may be controlled dependent on the observed or monitored functionally connected networks within the brain from EEG changes, indicative of an effective alteration or modification US 2020/0368491 A1 Nov. 26 , 2020 49 of the brain state or mental state in the subject. FIG . 10 thus [ 0412 ] FIG . 15 shows a flowchart of an embodiment of the shows a flowchart of an embodiment of the invention . A invention . The subject opens an app on , e.g. , a mobile or desired mental state is identified 540. The mental state of a wearable device and logs in to a personal account 1510. A subject identified 550 , and a phase of a dominant brainwave , new waveform , modified from the last waveform used based characteristic of the mental state of the subject identified on biometric sleep data received from the subject during a 560. A stimulus is applied to the subject to change the mental state of the subject to the desired mental state , while previous stimulation session 1520. Light and / or sound deliv synchronizing the phase of the stimulus with the phase of the ery through the device or through a wireless peripheral is dominant brainwave of the subject 570. The subject is chosen 1540. Sleep stimulation is turned using synchronized monitored to determine if the desired mental state is light and / or sound modulated with the chosen organic wave achieved . If the desired mental state is sleep , the sleep state form 1550. EEG and / or other biometric data is recorded of the subject may be verified 580 . from the subject and transmitted to a remote for analysis [ 0408 ] FIG . 11 shows a flowchart of a further embodiment 1560. The received biometric data from the subject is of the invention . An app is opened on a smartphone , tablet analyzed , to measure the effectiveness of the stimulation and or another mobile or wearable device 1110. Note that in to adjust the waveform accordingly, to improve the effect of some applications, the device need not be mobile , and for the stimulation 1570 . example may be part of a headboard , nightstand, dock radio , [ 0413 ] Therefore, statistical approaches are available for etc A soundtrack conducive to sleep , e.g. , sounds of rainfall, separating EEG signals from other signals, and for analyzing waterfall, ocean waves, a melody, white noise , pink noise , components of EEG signals themselves. According to the etc. , is chosen 1120. An organic waveform is chosen , derived present invention , various components that might be con from brainwaves of a sleeping donor, corresponding to a sidered noise in other contexts , e.g. , according to prior specific stage of a sleep cycle or a complete sleep cycle technologies, such as a modulation pattern of a brainwave , 1130. The sound delivery may be chosen to be through a are preserved . Likewise , interactions and characteristic mobile device speaker, earphones, wireless earbuds . If sepa delays between significant brainwave events are preserved. rate sound delivery to each ear, the sound may be isochronic This information may be stored either integrated with the tones or binaural beats 1140 , while if not isolated , isochronic brainwave pattern in which it occurs or as a separated tones may be played 1160 . modulation pattern that can then be recombined with an [ 0409 ] FIG . 12 shows a flowchart of a still further embodi unmodulated brainwave pattern to approximate the original ment of the invention . An app may be opened on a smart subject . phone , tablet or wearable device 110. Light settings , such as [ 0414 ] According to the present technology, lossy “ per color and intensity, are chosen 1220. An organic waveform ceptual " encoding ( i.e. , functionally optimized with respect derived from brainwaves of a sleeping donor, corresponding to a subjective response ) of the brainwaves may be to a specific stage or stages of sleep , or a complete sleep employed to process, store , and communicate the brainwave cycle is chosen 1230. Light delivery may be chosen through information . In a testing scenario , the “ perceptual” features an ambient light source or e.g. , LEDs positioned on a may be tested , so that important information is preserved wearable eye mask 1240 , which is wirelessly connected to over information that does not strongly correspond to the the device . Sleep stimulation is turned on by projecting the effective signal. Thus, while one might not know a priori light modulated with the chosen organic waveform through which components represent useful information, a genetic ambient light or LEDs positioned near the eyes 1250 . algorithm may empirically determine which features or data [ 0410 ] FIG . 13 shows a flowchart of an embodiment of the reduction algorithms or parameter sets optimize retention of invention . The subject opens an app on a device 1310 , and useful information vs. information efficiency. It is noted that chooses light and sound settings, e.g. , color, intensity, sound , subjects may differ in their response to signal components , volume , etc. 1320. An organic waveform derived from the and therefore the “ perceptual” encoding may be subjective brainwaves of a sleeping donor is chosen , e.g. , automatically with respect to the recipient. On the other hand , different by the app , corresponding to a specificstage ( s ) of sleep or a donors may have different information patterns, and there complete sleep cycle 1330. The stimulus is chosen as light fore, each donor may also require individual processing. As or sound delivery through the device or wirelessly 1340 . a result, pairs of donor and recipient may require optimiza Sleep stimulation is turned using synchronized light and tion , to ensure accurate and efficient communication of the sound modulated with the chosen organic waveform 1350 . relevant information . According to the present invention , [ 0411 ] FIG . 14 shows a flowchart of an embodiment of the sleep /wake mental states and their corresponding patterns invention . The subject opens an app on e.g. , a mobile or are sought to be transferred . In the recipient, these patterns wearable device 1410 , and chooses light and / or sound have characteristic brainwave patterns. Thus, the donor may settings, e.g. , color, intensity, sound, volume , etc. 1420. An be used , under a variety of alternate processing schemes, to organic waveform derived from the brainwaves of a sleeping stimulate the recipient, and the sleep /wake response of the donor is chosen , e.g. , automatically by the app , correspond recipient determined based on objective criteria, such as ing to a specificstage ( s ) of sleep or a complete sleep cycle resulting brainwave patterns or expert observer reports , or 1430. The stimulus is chosen as light and / or sound delivery subjective criteria, such as recipient self -reporting , survey or through the device or wirelessly 1440. Sleep stimulation is feedback . Thus, after a training period , optimized processing turned using synchronized light and / or sound modulated of the donor, which may include filtering, dominant fre with the chosen organic waveform 1450. EEG and / or other quency resynthesis , feature extraction , etc., may be biometric data is recorded from the subject and transmitted employed , which is optimized for both donor and recipient. in real time to the device or a cloud computing resource for In other cases , the donor characteristics may be sufficiently analysis 1460. The stimulation of the subject is adjusted normalized , that only recipient characteristics need be com based on the data received from the subject 1470 . pensated . In a trivial case , there is only one exemplar donor, US 2020/0368491 A1 Nov. 26 , 2020 50 and the signal is oversampled and losslessly recorded , [ 0418 ] The control for the stimulator is preferably adap leaving only recipient variation as a significant factor. tive and may employ a genetic algorithm to improve per [ 0415 ] Because dominant frequencies tend to have low formance overtime. For example, if there are multiple first information content ( as compared to the modulation of these subjects ( donors ), the second subject ( recipient) may be frequencies and interrelation of various sources within the matched with those donors from whose brainwave signals brain ), one efficient way to encode the main frequencies is ( or algorithmically modified versions thereof) the predicted by location , frequency, phase , and amplitude . The modula response in the recipient is best , and distinguished from tion of a wave may also be represented as a set of param those donors from whose brainwave signals the predicted eters . By decomposing the brainwaves according to func response in the recipient subject poorly corresponds. Simi tional attributes, it becomes possible , during stimulation , to larly, if the donors have brainwave patterns determined over modify the sequence of “ events ” from the donor, so that the a range of time and context and stored in a database, the recipient need not experience the same events, in the same selection of alternates from the database may be optimized order, and in the same duration , as the donor. Rather, a to ensure best correspondence of the recipient subject to the high - level control may select states , dwell times , and tran desired response . sitions between states , based on classified patterns of the [ 0419 ] It is noted that a resynthesizer -based stimulator is donor brainwaves . The extraction and analysis of the brain not required , if a signal pattern from a donor is available that waves of the donors , and response of the recipient, may be properly corresponds to the recipient and permits a suffi performed using statistical processes, such as principal com ciently low error between the desired response and the actual ponents analysis ( PCA ) , independent component analysis response . For example, if a donor and a recipient are the ( ICA ) , and related techniques; clustering, classification , same subject at different times , a large database may be dimensionality reduction and related techniques; neural net unnecessary , and the stimulation signal may be a minimally works and other known technologies. These algorithms may processed recording of the same subject at an earlier time . be implemented on general purpose CPUs , array processors Likewise , in some cases , a deviation is tolerable, and an such as GPUs , and other technologies . exemplar signal may be emitted , with relatively slow peri [ 0416 ] In practice, a brainwave pattern of the first subject odic correction . For example, a sleep signal may be derived may be analyzed by a PCA technique that respects the from a single subject and replayed with a periodicity of 90 non - linearity and non -independence of the brainwave sig minutes or 180 minutes, such as a light or sound signal , nals , to extract the major cyclic components , their respective which may be useful in a dormitory setting, where individual modulation patterns , and their respective interrelation . The feedback is unavailable or unhelpful. major cyclic components may be resynthesized by a wave [ 0420 ] In some cases , it is useful to provide a stimulator form synthesizer, and thus may be efficiently coded . Further, and feedback -based controller on the donor. This will better a waveform synthesizer may modify frequencies or relation match the conditions of the donor and recipient, and further ships of components from the donor based on normalization allow determination of not only the brainwave pattern of the and recipient characteristic parameters. For example , the donor but also responsivity of the donor to the feedback . brain of the second subject ( recipient) may have character One difference between the donors and the recipients is that istic classified brainwave frequencies 3 % lower than the in the donor, the natural sleep pattern is sought to be donor ( or each type of wave may be separately parameter maintained and not interrupted . Thus, the adaptive mufti ized ), and therefore the resynthesis may take this difference subject database may include data records from all subject, into account. The modulation patterns and interrelations whether selected ab initio as a useful exemplar or not . may then be reimposed onto the resynthesized patterns. The Therefore , the issue is whether a predictable and useful normalization of the modulation patterns and interrelations response can be induced in the recipient from the database may be distinct from the underlying major cyclic compo record and, if so , that record may be employed . If the record nents, and this correction may also be made , and the would produce an unpredictable result or a non - useful result, normalized modulation patterns and interrelations included the use of that record should be avoided . The predictability in the resynthesis. If the temporal modifications are not and usefulness of the responses may be determined by a equal, the modulation patterns and interrelations may be genetic algorithm or other parameter -space searching tech decimated or interpolated to provide a correct continuous nology time sequence of the stimulator. The stimulator may include [ 0421 ] Extending the sleep signal illumination example , one or more stimulation channels , which may be imple an illuminator ( e.g. , red LED lightbulb ) may have an inten mented as electrical, magnetic, auditory, visual , tactile , or sity modulated based on a donors ' brainwave pattern . The another stimulus, and / or combinations . illuminator may have a flash memory module with tens or [ 0417 ] The stimulator is preferably feedback controlled . hundreds of different brainwave patterns available . The The feedback may relate to the brainwave pattern of the illuminator may further include a sensor, such as a camera recipient, and / or context or ancillary biometric basis . For or non - imaging optical or infrared sensor , and speech con example , if the second subject ( recipient) begins to awaken trol, similar to Amazon Alexa . The illuminator may also from sleep , which differs from the first subject ( donor) sleep include an associated speaker, to play synchronized sounds pattern, then the stimulator may resynchronize based on this or music . When a sleep cycle is commenced , the illuminator finding. That is , the stimulator control will enter a mode begins displaying and playing and associated audio ) the corresponding to the actual state of the recipient, and seek to brainwave pattern as a program , seeking to induce a prede guide the recipient to the desired state from a current state , termined sleep pattern . The sensors may be used to deter using the available range and set of stimulation parameters . mine whether the recipient is in the predicted sleep state The feedback may also be used to tune the stimulator, to based on the program . If the recipient has a sleep state that minimize error from a predicted or desired state of the deviates from the program , then the program may be reset to recipient subject based on the prior and current stimulation . a portion that corresponds to the actual state of the recipient US 2020/0368491 A1 Nov. 26 , 2020 51 or reset to a guiding state that seeks to guide the sleep state transceiver module for processing all wireless data trans of the recipient back to the desired program . If the target mitted from and to a wireless communications network . It subject cannot be efficiently synchronized or guided , then can be appreciated that other variations for wireless trans the illuminator may adopt a different source subject brain ceiver module 17 can also be provided , such as standardized wave pattern . In this case , no electrical stimulation or Bluetooth , NFC , Zigbee , etc., and proprietary RF protocols electrical feedback is employed , and the entire operation that may be developed for specialized applications. may be non -contact . [ 0424 ] Port 12 can be connected to CPU 10 and can be [ 0422 ] FIG . 16 shows a representation of a mobile device temporarily attached , for example, to a docking station to 11. The mobile device is shown in a familiar “ smadphone ” transmit information to and from the mobile device 11 to form factor. Data can be transferred to and from the mobile other devices , such as personal computers. In light of the device 11 via wireless data communications . In general, the present invention , port 12 can also be connected to external mobile device 11 can include a touch - sensitive display probes and external sensors for monitoring or providing screen 18 , a speaker 30 , a microphone 31 , and one or more data . Port 12 can also be configured , for example to link with control buttons 32 for controlling some operations of device a battery charger, data communication device , and can 11. The device 11 depicted in FIG . 1 ( a ) can be a device , such permit network devices, a personal computer, or other as , for example , a smartphone capable of communicating computing devices to communicate with mobile device 11 . with a wireless local area network , and so forth . In this [ 0425 ] User controls 32 can permit a user to enter data to respect , the mobile device 11 can be implemented with touch mobile device 11 and / or initiate particular processing opera screen capabilities associated with the display screen 18 . tions via CPU 10. A user interface 33 can be linked to user Display screen 18 can be configured to display data includ controls 32 to permit a user to access and manipulate ing video and text and icons 33 operable as soft buttons electronic wireless hand held multimedia device 11 for a providing options and action by the mobile device 11 when particular purpose , such as , for example , viewing video selected by a user . The mobile device 11 can be capable of images on display 18. User interface 33 can be implemented carrying out a variety of functionalities. For example, micro as a touch screen manipulated user interface , as indicated by processor shown as CPU 10 of the mobile device 11 can the dashed lines linking display 18 with user interface 33 . function as the main controller operating under the control User interface 33 can be configured to accept user input into of operating docks supplied from a dock oscillator. CPU 10 the mobile device 11. In addition , CPU 10 can cause a sound can be configured as , for example , a microprocessor . Such a generator 28 to generate sounds of predetermined frequen microprocessor can be configured to facilitate the operations cies from a speaker 30. Speaker 30 can be utilized to produce of and communicate by the electronic wireless hand -held music and other audio information associated with video multimedia device 11. External pins of CPU 10 can be data transmitted to mobile device 11 from an outside source . coupled to an internal bus 26 so that it can be interconnected [ 0426 ] AGPS ( Global Positioning System ) module 13 can to respective components . The mobile device 11 can also be be included in the mobile device and can be connected to bus configured to include memories such as , for example , 26. GPS module 13 can be configured to provide location SRAM 24 which can be provided as a writeable memory that information for the mobile device 11 and can operate with does not require a refresh operation and can be generally mapping software and resources to provide navigable direc utilized as a working area of CPU 10 , SRAM ( Static RAM ) tions on the display screen 18 to the user , which can be is generally a form of semiconductor memory (RAM ) based referred to as GPS mapping . The CPU 10 can execute on a logic circuit known as a flip - flop , which retains infor " apps ” , which are downloadable programs that provide a mation as long as there is enough power to run the device . user interface , and access to various application program Font ROM 22 can be configured as a read only memory for ming interface (API ) calls made available through the oper storing character images ( e.g. , icons and font) displayable on ating system , but are generally limited to executing in a low a display 18 , which can be implemented as , for example , a privilege mode and without direct hardware or driver level touch - sensitive display screen . Example types of displays access . The apps may be downloaded from the Internet, or that can be utilized in accordance with display 18 include, an on - line service ( e.g. , iTunes Store, Google Play ) or for example, a TFT active matrix display, an illuminated through a wireless transceiver . LCD ( Liquid Crystal Display ) , or other small - scaled dis [ 0427 ] FIG . 17 shows a hypnogram of a healthy adult . As plays being developed or available in the art in compact shown, the sleep cycle progresses non -monotonically form . CPU 10 can be utilized to drive display 18 utilizing , through a series of stages . among other media , font images from Font ROM 22 and [ 0428 ] FIG . 18 shows a hypnogram of a healthy adult. As images transmitted as data through wireless unit 17 and shown , one sleep cycle lasting approximately 90 min is processed by image - processing unit 35. EPROM 20 can be comprised of several sleep stages , including REM sleep ( R ) , configured as a read - only memory that is generally erasable first non - REM stage (N1 ) , second non -REM stage ( N2 ) , and under certain conditions and can be utilized for permanently third non -REM stage (N3 ), also known as slow - wave sleep , storing control codes for operating respective hardware having different duration and periodicity. The waking stage components and security data , such as a serial number. A is indicated on the hypnogram as W. camera capable of capturing video and pictures can be [ 0429 ] FIG . 19 shows a flowchart indicating the sequence provided and can also work in conjunction with the image of sleep stages. processing unit 35 . [ 0430 ] FIGS . 20A - 20C show a sample of the REM stage [ 0423 ] IR controller 14 , when provided , can be generally of sleep in a 34 year - old female under different filtering. This configured as a dedicated controller for processing infrared sample is obtained from the database of Sleep EEG record codes transmitted / received by an IR transceiver module 16 ings described in B Kemp, AH Zwinderman , B Tuk , HAC and for capturing the same as computer data . Wireless unit Kamphuisen , JJ L Oberyé. Analysis of a sleep - dependent 17 can be generally configured as a dedicated controller and neuronal feedback loop : the slow - wave microcontinuity of US 2020/0368491 A1 Nov. 26 , 2020 52 the EEG . IEEE - BME 47 ( 9 ) : 1185-1194 ( 2000 ) has been case that an element is “ directly connected or coupled to " used . For each sleep /wake state of each subjects, 1260 another element and a case that an element is “ electronically seconds samples have been obtained ( totaling up to 72 connected or coupled to ” another element via still another samples per subject, totaling 4898 samples ). Only one element. Further, it is to be understood that the term " com channel ( Fpz - Cz ) has been considered . The samples have prises or includes ” and / or " comprising or including ” used in been cleaned from noise /non - stationary component using the document means that one or more other components, singular spectrum analysis , see Singular Spectrum Analysis steps , operation and / or existence or addition of elements are with R. Springer. 2018 Authors : Golyandina, Nina, not excluded in addition to the described components , steps , Korobeynikov, Anton, Zhigljaysky, Anatoly, generally fol operation and / or elements unless context dictates otherwise . lowing approach of Neurosci Methods . 2016 Nov. 1 ; 273 : [ 0441 ] Through the whole document, the term “ unit " or 96-106 . doi : 10.1016 / j.jneumeth.2016.08.008 . Epub 2016 “ module ” includes a unit implemented by hardware or Aug. 12 ; Improving time - frequency domain sleep EEG software and a unit implemented by both of them . One unit classification via singular spectrum analysis. Mahvash may be implemented by two or more pieces of hardware , Mohammad , Kouchaki, Ghavami, Sanei . Data analysis and two or more units may be implemented by one piece of showed that the use of just 16 55A components is sufficient hardware . to preserve waveform spectrum of the EEG recordings, [ 0442 ] Other devices , apparatus, systems , methods, fea whereas 64 55A components is enough to very precisely tures, and advantages of the invention will be or will become match the shape as well . Restoration with the use of 64 apparent to one with skill in the art upon examination of the components has been used to generate “ filtered ” EEG following figures and detailed description . It is intended that samples. all such additional systems, methods, features , and advan [ 0431 ] FIG . 20A shows the original , FIG . 20B shows tages be included within this description , be within the scope sample reconstructed with 64 SSA groups, and FIG . 20C of the invention, and be protected by the accompanying shows the sample reconstructed with 16 SSA groups, for a claims . sample of REM phase of 34 - year female. [ 0443 ] In this description , several preferred embodiments [ 0432 ] FIGS . 21A and 21B show an EEG for a 30 years were discussed . Persons skilled in the ad will , undoubtedly , old female in sleep stage R. have other ideas as to how the systems and methods [ 0433 ] FIG . 22 show an EEG for a 30 years old female in described herein may be used . It is understood that this sleep stage 3 . broad invention is not limited to the embodiments discussed [ 0434 ] FIGS . 23A and 23B show an EEG for a 30 years herein . Rather , the invention is limited only by the following old female in sleep stage 3 . claims . [ 0435 ] FIGS . 24A and 24B show an EEG for a 25 years [ 0444 ] The aspects of the invention are intended to be old female in sleep stage W. separable and may be implemented in combination , sub [ 0436 ] FIGS . 25A and 25B show an EEG for a 25 years combination , and with various permutations of embodi old male in sleep stage 2 . ments . Therefore , the various disclosure herein , including [ 0437 ] FIGS . 26A and 26B show an EEG for a 25 years that which is represented by acknowledged prior art, may be old male in sleep stage 1 . combined , sub - combined and permuted in accordance with [ 0438 ] FIGS . 27A and 27B show an EEG for a 25 years the teachings hereof, without departing from the spirit and old male in sleep stage W. scope of the invention . All references and information [ 0439 ] See Reference List Table 19 sources cited herein are expressly incorporated herein by [ 0440 ] Through the whole document, the term “ connected reference in their entirety. to ” or “ coupled to ” that is used to designate a connection or [ 0445 ] Each reference is expressly incorporated herein coupling of one element to another element includes both a by reference in its entirety US 2020/0368491 A1 Nov. 26 , 2020 53

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App. Nos. 5,293,187,5,422,689 ;5447,166,5491,492,5,546,943,5,622,168,5,649,061 ; 5720619,5,740,812,5,983,129, 6,050,962, 6,092,058 , 6,149,586; 6,325,475 ;6377,833 ; 6,394,963, 6,428,490; 6,482,165; 6,503,0856,520,921; 6,522906 ;6,527,730 ; 6,556,695,6,565,518 ;6,652,458 ; 6,652,470; 6,701,173 ; 6,726,624,6,743,1826,746,409,6,758,80 ; 6,843,774,6,896,655,6,996,261; 7,037,2607,070,571,7,107,090,7,120,486,722,851,7,215,994,7,260,430,7,269,455, US 2020/0368491 A1 Nov. 26 , 2020 61

7,280,870 ; 7,392,079,7,407,485 ; 7,463,142; 7,478,108,7,488,294,7,515,054 ;7,567,693,7,647,097,7,740,592 ; 7,751,877,7,831305; 7,856,264 ; 7,881,780; 7,970,734 ; 7.972278 : 7.974.787.7.991461: 8.012107: 8,022,486: 8.033.996: 8.060.194.8.095 209.8.209.224:8.239.030:8262714:8.320.649.8.358.818 8.376,965: 8380.316 8386312838633; 8,392,250; 8,392288392,254,8392255; 8,437,844,8,464,288,8,475371: 8,483,816,8,494,905,8517,9285330428545,420,8,560,04 ; 8.655,428,8,672,852,8682687,8684,742,8694,157,8,706,241,8,706,58; 8,738,395,8,753,296,8762,202,876467,8,768,022 8788,0308720,25,8790,297 ; 8,821,376; 8,838,247,8,864,310,8872640 ; 8,888,723; 8,915,871,8,938,289,8,938,301; 8,942,813,8,955,010,8,955,974,8,958,882, 8,964,298 ; 8,971,96; 8,989,835, 8,992,80,8,998,828,9,004,687,9,060,671; 9,101,279; 9,35,221; 9142145 ; 9,165,472, 9,73,582, 9,179,855 ; 9,208,558 ;9,215,978 ; 922,984 ; 9,241,665; 9,242067, 9.254.099 ; 9.27,660,9,275,191,9,282,927,9.292858 9.292,920 : 9320,450,9326,705; 9,330,206; 9.357,94 : 9,396.669,9398,873 : 9,44780: 9,414,907.9.424,761; 9.445739,9,4457639,451303 9,451,899,9,454,6469,462.977,9,468,541,9,483,117,9,492 20: 9,504,420,9504,788; 9,526,419,9,541,383,9545221; 9545,222 9,545225 : 9,560,967,9,560,9849,563,740 : 9,582072 9596,224 : 9615746 : 9622,702 9,622703 : 9,626,756,9629,568,9642699,9649,030 : 9,661368 9,655,573; 9,668,694; 9,672,302 9,672,617,9,68232 ; 9,693,734; 9,694,155,9,704,205; 9,706,910; 9,710,788; RE44408; RE45766; 20020024450; 20020103428 20020103429; 20020112732 ; 20020128540; 20030028081; 20030028121; 20030070685 ; 20030083596 ; 20030100844 ; 2003012017220030149351; 20030158496 ; 20030158497, 20030171658 ; 20040019257,20040024287, 20040068172, 20040092809 ; 20040101146 ; 20040116784 ; 20040143170 ; 2004026752, 20050010091 ; 20050019734; 20050025704,20050038354 : 200501137B , 20050124851,20050148828 ; 20050228785 ; 20050240253,20050245796 ; 20050267343; 20050267344; 20050283053 ; 20060020184 ; 20060061544; 20060078183 ; 20060087746; 20060102171; 20060129277,20060161218; 20060189866 ; 2006020000 : 20060241718; 2006062978 ; 20060252979,20070050715, 200701799420070191704,20070238934,20070273611; 20070282228 ; 20070299371; 20080004550,20080009772; 20080058668, 20080081963; 20080119763; 20080123927 ;2008032383 ; 20080228239,20080234113; 20080234601; 20080242521; 20080255949, 20090018419; 20090058660: 20090062698 ; 20090076406; 21090099474 ; 20090112523; 20090221928; 20090267758; 20090270687,20090270688; 20090270692, 20090270693; 20090270694; 2009020786; 20090281400 ;20090287108 ; 20090297000; 20090299169; 20090311655; 20090312808, 20090312817 ; 20090318794 ; 20090326604 ; 20100004977 ; 20100010289 ; 20100010366 ; 20100041949; 20100069739,20100069780; 20100163027,20100163028 ; 200019303520100165593,20100168525 ; 20100168529,20100168602 20100268055; 20100293115/20110004412 20110009777, 20110015515; 20110015539; 20110043759; 20110054272 ; 20110077548; 20110092882; 2010105859 ; 2011010643; 20110172500 ; 20110218456: 20110256520; 2010270074/20110301488, 20110307079.20120004579,2012002394 ; 20120136004; 201200777 ; 20120108909,20120108995 ; 20120136274: 20120150545 ; 2012020330; 20120262558 ; 2012027377,20120310106; 2030012804 ; 2030046715; 20130063434; 20B0063550 ; 20130080127; 20B0120246; 20130127980 ; 2030185144 : 2030189663/20130204085: 20130211238 , 20BC226464 ; 2030242262, 20130245424,2030281759,20B0289360: 20B0293844 / 20130308099, 2030318546,20140058528,2014015571420140171757,20140200432 20140214335; 20140221866; 20140243608 ; 20140243614,20140243652,2014027630 20140276944; 20140288614 ;20140296750 ;20140300532 20140303508; 20140304773 ; 20140313303 ;20140315169 , 20140316191 ; 20140316192 20140316235; 20140316248; 20140323899; 20140335489 ; 20140343408,20140347491; 20140350353; 20140350431; 20140364721; 20140378810 ; 20150002815; 20150003698; 20150003699; 20150005640 ;20150005644 ; 20150006186 ; 20150012111; 20150038869; 20150045606 ; 20150051663; 20150099946 ; 20150112409; 20150120007 ; 20150124220,20150126845 20150126873,201503382,20150141773,20150145676,2060154889, 20150174362,20150196800,20150213191,20150223731; 20150234477 ; 20150235088,20150235370 ;20150235441 ; 201502354147,20150241705 ; 20150241959; 20150242575; 20150242943; 20150243100 ; 20150243105; 2015024306; 20150247723 , 2015024797 ; 20150247976 ; 20150248169; 20150248170 ;20150248787,20150248788 ; 20150248789; 20150248791; 20150248792 20150248793 ; 20150290453; 20150290454 ;20150305685 ; 20150306340; 20150309563 ; 20150313496 ; 20150313539; 20150324692; 20150325151 ; 20150335288; 2015033963 ;20150351690 ;20150366497 ; 20150366504 ; 20150366656; 20150366659; 20150369864 ;20150370320 ; 20160000354 ; 20160004298 ; 20160005320; 2016000791520160008620; 20160012749,20160015289,20160122167,20160022206,20160029946,20160029965 ; 20160038069,20160051187,20160051793 20160066838 ; 20160073886 ; 20160077547; 20160078780; 20160106950 ; 20160112684 ; 20160120436; 20160143582; 20160166219, 201601676720160176053 ; 20160180054 ; 20160198950/ 20160199577,20160202755; 2016026760,20160220439 20160228640,20160222625 ;20160232811 20160230 23 :2016029084 ; 20160248994; 20160249826,20160256108 ; 20160267809 ; 20160270656 ;20160287157,20160302711 ; 20160306942; 20160313798 ; 20160317060,20160317383; 20160324478; 20160324580 ; 20160334866; 20160338644; 20160338825; 2016033900 ; 20160345901; 20160357256,20160360970 ; 20160363483; 20170000324 ; 20170000325 : 20170000326,20170000329,20170000330; 2070000331,20170000332 20170000333 ; 20170000334,20170000335 20170000337,20170000340; 20170000341; 20170000342 ; 20170000343 20170000345 : 20170000454 ; 20170000683,20170001032, 2017000631; 20170007111 ; 20170007115,20170007116 20170007122,207000712 ; 20170007165 ; 20170007182; 20170007450,20170007799,20170007843; 2070010469,20170010470 ;20170017083 ; 20170020447; 20170020454; 20170020627; 20170027467,20170027651; 20170027812 20170031440; 20170032098; 20170035344 ; 20170043160,20170055900,20170060298 ; 20170061034,20170071523,20170071537,20170071546,20170071551; 20170080820, 2070086729,20170095157,20170099479,20170100540 20170103440; 20170112427,20170112671; 2017013046 ;2017013056 ; 20170119994,207015597,2017035633; 20170136264,2070136265; 2010143249,20170143442 20170148340,20170156662,20170162072 20170164876 ; 20170164878; 20170168568; 20170173262,2017073326 ; 20170177023 ; 20170188947; 20170202633 20170209043,20170209094 ; and 20170209737 . Reference List Table5 U.S. Patents and Pub. App. Nos. 3.951,34,4,437,064,4,591,787.463,817,4,689,559,469,000 ; 4,700,35; 4,73,180: 4,736,751; 4,749,946,4,75,246 ; 4,761,611; 4 771,239,4801,882,4,862,359; 4,98,152,4937525 :4940058 ; 4,947,480,4949,725 ; 4,951,674,4974,602, 4982157,498,92 4996,479,5008,622 5012,190 5,020,58; 5,061,680 5,092,835,5095,270,5,12635; 5158 , B2,5,159,703 ; 5,159,928 5,166,6145,187327 ; 5,198,977,52B338,5241,967,5243,281,5243517, 5.263,488: 5,265,611; 5.269,325 : 5,282,4745,283,5235,291.888; 5303,705 ; 5,307,807,5309,096 ;5311,29,532,777,5325,8625326,745 ;5339,811 ; 5,417,211; 5,418,512 ; 5,442,289; 5,447,154,5,458,142; 5,469,057 ; 5,476,438 ; 5,496,798; 5,513,649; 5,515,301; 5,552,375; 5,579,241; 5,594,849 ; 5,600,243; 5,601081; 5,617,856 ; 56261455656,937,567,740 ; 5682,889,5,701,909,5706,402 ;5706,811 ; 5729,046; 5,743,854 ; 574,860,5,72,54,5,72911; 5755227,5,761,32,5762611; 5,767,043,5,771,261,5,77,893,5,77,8945,797,853,5,813,993 ; 5,815,409 ; 5,842,986 ; 5,857,978 : 5,885,976 ; 5.921.245; 5,938,598 ; 5,938,688; 5,970,499; 6,002,254 , 6011,99 ; 6,02,161,6066,0846,069,3696,080,164,6099,319,6,144,872 6,154,026 ;6155,966 ; 6,167,298 6,167311; 6195576 ; 6,230,0376239,1456,263,189 6.290.638 : 6.354.087.6.356.079 :6370,414 :6374,131 : 6385.479 :6,4183446,442948 : 6,470.2206,488.617 :6562466.526,415 : 6.529.759.6538.436 : 6.59.245 6,539,263, 6,544,170,6,547,746 ; 6,557,558; 6,587,729; 6,591, B2 , 6,609,030,6,611,698 ;6.648,82,6,658,287,6,665,552 ; 6,665,553, 6,665,562 6,684,098 ;6,687,525 ; 6,695,761; 6,697,660 ; 6,708,051 ; 6,708,064; 6,708,184 ;6,725,080 ; 6,735,460,6,774,929,6,785,409,6,795,724,6,804,661; 6,815,949,6,853,186 ; 6,856,830,6,873,872; 6,876,196 ; 6,885,192,6,907,280 : 6,926,921; 6,947,790 : 6,978,179 :6,980,863 : 6,983,184,6,983,264,6,996,261; 7,022083; 7,02,206,7,024,247,7,035,686,7,038,450;