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Brain–Computer Interfaces Handbook Technological and Theoretical Advances Chang S. Nam, Anton Nijholt, Fabien Lotte

Passive Brain–Computer Interfaces

Publication details https://www.routledgehandbooks.com/doi/10.1201/9781351231954-3 Laurens R. Krol, Lena M. Andreessen, Thorsten O. Zander Published online on: 10 Jan 2018

How to cite :- Laurens R. Krol, Lena M. Andreessen, Thorsten O. Zander. 10 Jan 2018, Passive Brain–Computer Interfaces from: Brain–Computer Interfaces Handbook, Technological and Theoretical Advances CRC Press Accessed on: 04 Oct 2021 https://www.routledgehandbooks.com/doi/10.1201/9781351231954-3

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BCI aname: passive proposed and BCI research traditional approach from this separating quently aframework formally presented (Cutrellindependently of other &Tan 2008a). each (2008b) et al. et al. Zander Zander 2008; subse Italy), Florence, endeavor conference (CHI 2008, same research atthe while groups by two research BCI methodology for implicit input benefit to ongoing HCI was - explicitlyas a worth proposed of use The attention. widespread approach gained the overall, that enjoyed popularity increased But general. when in BCI it was (HCI) 2008, only in interaction human–computer 2008a) during et al. (Zander errors 2003) machine et al. and (Parra suggested was for also errors response This 2008).2005, Millán & al.2002b; Ferrez et (Blankertz BCI systemstraditional of sificationerrors discussion.) belowsee for afurther its biological automatic, in “spontaneous” instinctive, involuntary, sense, meaning inattentive; and 2014). et al. Zander 2009; asystemet to al. (Rötting input implicit as used (We be could then use activity, detections investigate these automatic and brain that detect to applied spontaneous, and be methodology could also this that user, of conditioning idea the was the rekindled operant replaced by machine recognition the 2002a). et al. pattern As (Blankertz recordings on initial based learning machine using supervised machine, the to 1999). et shifted al. has (Birbaumer days, These training frequencysite ata modulation specific electrode such aspecific EEG, as their in features detectable Previously, 2000). et al. Ramoser machine- specific generate to of users BCI systems were trained 2007; et al. (e.g., Lotte millennium 2002a; et al. current of the Blankertz start atthe techniques learning introduction machine of the was significance methodology more reliable. particular Of applications.these to connected inseparably be to & McFarland 2008). appeared For fact, BCI along research in time, (e.g., 2016; et al. theses Soekadar 2008; Wessberg Wolpaw Müller-Putz &Pfurtscheller 2000; et al. 2010;Erp 1988; &Donchin Farwell Treder 2011) et al. 2009; et al. Furdea - pros brain-activated and speller devices (e.g., mental different in alia, inter resulted, 1999; et al. Birbaumer Brouwer &Van control, and and astrong to focus led technology communication on BCI for this most from direct muscles their use to (Wolpaw resolve 2002). et al. This help to people who those benefit to stood outside the with world people motor-impaired communicate to without having lyzed or otherwise - for provide BCIs para could ameans potentially such direct that Itattention. was quickly realized widespread methodology gained (BCI) interface context brain–computer it that aclinical was in time. real in activity brain specific (and continuously decisionlearned system such, As strategies. the updated) was able recognize to belongingas to one data previouslyof on EEG categories, based four computer classifiedincoming problem” detection (EEG), 1977):nal a (Vidal electroencephalogram an to continuous with access asig as experiment activity, the investigate to brain ods suggested Vidal “treating neuroelectric - meth example ofposteriori control to a asystem. using of used offline, how be Instead might these 1977,In events” of offered detection an “brain and real-time Jacques the J. demonstrated Vidal 3.1 70 A passive BCI (Zander et al 2008b; Zander &Kothe 2011)A passive 2008b; Zander et al BCI (Zander system derives its auto output from clas- found suggestions be correct in to examples can Early error-related use to potentials of this and BCI Improvements over have made field decades the interdisciplinary the made more approach, applications of this future upon awide of range potential speculated Vidal Although technology.neuroadaptive development and research possibilities for passive into future and BCI, implicit interaction, relevant are for that design of the to pBCI-based aspects systems points interaction and human–computer helps categorization highlight to formal and perspective systems. This

PASSIVE BRAIN–COMPUTER INTERFACES PASSIVE BRAIN–COMPUTER . The concept found acceptance also among clinical BCI researchers BCI researchers clinical among also concept found. The acceptance Brain–Computer Interfaces Handbook Interfaces Brain–Computer - - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 ity. The chapter ends with a brief speculation of the future and a summarizing conclusion. asummarizing and ity. future of speculation the abrief chapter ends with The given abovementioned interactiv sequence, the the examples toward illustrate trend increased thus helped by be might asystem category. each from theater gesting how operating In the asurgeon in chapter, the sug usthroughout exampleferent accompanies hypothetical categories. A(partially) dif given the are illustrate to previous research the one. current examplesSelected and past from to compared of interactivity complexity.degrees in increase an represents category presented Each aneurophysiological but input, take respond it to varying of with their all systemsries as signal that machine. the and user human the work passive to recent between thought related in and interactivity BCI: toward atrend increased observed be can we that chapter. atrend intention,woulddiscuss to of not this the Instead, and like comprehensive of applications, approach a to widea truly variety overviewthis is beyond scope, the of applicability general of Because the ofinput) directions. arange into different research to led has cue to or achieve used state. are that stimuli BCI, external addition, when, in conscious intention the of with a reactivefor a relaxation influencing isBCI induced system—or (re)active system not on the or passive and user itself. individual on the depends ultimately BCI system. the to cater Note, however, of categorization aBCI system the as measure, by this that to modulated is not purposefully that activity human natural from arises activity underlying brain chapter: the this in discussed technology of the property acore as is taken input. This as function its from activity. it dissociated human from is implicit then input gathered The “natural” represent influence answeroverthe “yes,” a is If system. activity behaviorthen and brain maywe this say that was user not would aware the activity if of observed be their behavior user brain same and the we processing. whether athought experiment, could ask As cation of of relaxation for astate further Dictionary purpose intended the meditation, intentions. In align. factors contextual only achieved is usually when that astate induce to attempt of it voluntary both: is the ous mixture ambigu example, by user. present to controlled an an the voluntarily as seems Meditation, fully is not active that activity way an of, use rely BCI might make brain on, or inadvertently around, other The expected. not as are results if activity consciously to attempt this also might modulate place. A user takes “spontaneous” activity the that sure make to resources attentional commit tarily a passivetheyhave the BCI systemby expectations influenced be that might andsystem of volun by used anyultimately BCI system may not precisely fit one such category. Auser who is aware of neous” versus “voluntary,” definitions byof these BCI systems. activity required the Similarly, as (e.g., 1988). &Donchin Farwell by shifts; attention modulated amplitudes P300 attention voluntary through modulated is evoked that activity but indirectly stimulation by external &Neuper 2001), BCIs; Pfurtscheller imagery reactive with and application (e.g., control to order an in modulated questionin is consciously purposefully and motor relates of relevant cognitive or affective states. for activity cor informative brain apassivewhile their monitors background, BCI system, the in explicitly, athand activity. focuses user task elicit on the the or voluntarily this or modulate Instead, actively, the user to no effort that exerts aspect activity, defining and brain it inherent istaneous an end automatic,of BCI: ofunderlying user being signals the a system spon the the to respect with Use word of the “passive” auser-centered on perspective HCI. It from results here refers role the to 2009). et al. ongoing (Rötting an implicit task as input support to used is activity then interpreted Passive Brain–Computer Interfaces Brain–Computer Passive “Interactivity” denotes “the ability of acomputer ability respond auser’s to to “the denotes “Interactivity” ( input” implicit passive as activity the last decade, brain the BCI approach (i.e.,In human using natural for active used be an fact BCI—when of example also in astate the can of meditation Indeed, The active/reactive/passiveThe focuses is a user-orientedhere one and on used conscious distinction “sponta as - categorized readily be can states mental that when presuming should taken be Care Kothe (2011) and Zander passive contrast BCI active with , Oxford University 2016). Press following four the catego In describe we sections, will of the activity is activity relaxation itself,of the communi not the BCI systems, where the brain activity activity BCI systems, brain where the BCI systems, which rely on brain Oxford English Oxford English 71 ------Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 is assumed to be behaving naturally, as discussed in Section 3.1. in behaving discussed naturally, as be to is assumed user The further. & Kothe 2011). studied and analyzed be then can information state resulting The is, for that states, of mental avariety concerning information obtain to used be BCI thus methodologysubset can of traditional A who measured. is being human back the to reported system are changes that but no causes further the measurements merely case, Translation, quantifies this in any computer initiated. actions being surprise. and examples arousal, include Other workload, vigilance, perceive, stress, do, or think. we result of the something but as for voluntarily experienced induced example, not usually are consciousthe intentionthe ofBCI system influencing discussionthe in Section (see 3.1).Emotions, activity, perception, result without of or thought, the anatural as automatically it arise to requiring selection or the of on ascreen. aletter arm computer action aspecific into (output), translated is then for the example,movement prosthetic of a activity) the measured from (inferred detection The of state such amental state. mental aspecific to activity, of brain aspects which reflectcorrelate or ofinput focuses measurement particular on the (Wolpaw former on the definition, based latter this the produces In 2000). et al. that tion algorithm - transla components: and a BCI by systemsinput, general, consist oneoutput, definition, in three of 3.2.1 3.2 72 example, to fill out a workload-measuring questionnaire. Workload has been widely recognized as a widely questionnaire. been example, outhas recognized a Workload fill to workload-measuring collection, for for data interrupted would be to operator ergonomics where need the tional methods operator’s an assess to order in of use methods BCI-based the workload levels may replace tradi neuroergonomics advantages. In have methods - dis clear where more traditional useful particularly be can monitoring state Mental e 3.2.2 such in activity. or decreases saw operation of increases phases the local it activity. may evendesigns brain evoked what seen Furthermore, be least workload-correlated the which three of the determined be it can low data, of and high gathered workload. on the Based sessions controlled carefully activity, of brain her during recordings collected different to compared later are activity brain of this Features is activity registered. brain her designs, while three using all operation the same most be efficientperforms conditions.designsThe under surgeon will working isgoal evaluateto designs which The robot. for different of these surgical a new teleoperated three it of by agiven aquantitative state. producing measure mental were most features or least pronounced. the at once, moment but also recording by the for moment, identifying, example, during atwhat times be done engagement-related can completefor quantification recordings were This present. features to what these recordings, extent quantifies, for other that generated be index can an then recordings, those in neurophysiological robustly and detected can be state ofexperimentally that correlates one(s). induced experimentally the for If, example, of engagement astate reliably can induced be what to extent environment, operator’s adifferent the in anew from assess, recording reflected state ablebe to in order later to approach, the BCI general per as performed then are fier calibration classi and extraction, Signal processing, feature induced. and controlled carefully be can state operator’s where an environments experimental mental in gathered usually are data calibration To perform such an analysis, that is, to calibrate such a “state detector” or classifier suchTo detector” a“state is, calibrate to analysis, such that an perform by informative helpful itself, and be however, can of states measurement such mental The without is measured, that state exact mental the to cause ofPassive the respect with distinct BCIs are To with asurgeon is presented give example imagine of used, ahypothetical could be how this of neurophysiological arecording assessment takes state interprets brief, and activity mental In

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(Müller Zander Brain–Computer Interfaces Handbook Interfaces Brain–Computer , individual individual , - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 Passive Brain–Computer Interfaces Brain–Computer Passive 3Dalbeitdifferentsensitivities. wayfinding with game, difficulty levels across significantly ina differ measures found both They attention. that measure to experimentally controlled variant of the n of the variant controlled experimentally (Kok 2001). task oddball the in following ERP stimuli component the target in P300 the of decrease amplitude an in reflected is then attention decreased This task. secondary invested the be in less can attention the task, more cognitive by primary The the load is demanded task. of on another, low-pitch primary focus to primarily tones), attention participants but instruct (e.g., task ary of number relatively the count to high-pitch amonotonous tones among rare sequence - asecond as paradigm oddball an is pose to example, workload method alternative measurement an by ERPs’ morphology cognitive affective the may 2014). (Luck modulated and be and processes For atevent-related looking recordings, of (ERPs). EEG domain potentials time neurophysiological(frustration), measured the to differences. contributing control felt lost of had they loss where participants phases control over in measurement This interaction. the a to baseline Kullback–Leibler compared the HCI task, divergence increased features, of recorded gamified a goal-oriented, lyzed more closely paradigm, their underlyingthe In out find to factors. - divergence ana be Such then adetected can performance. of suboptimal measure undirected as (e.g., workload). Jatzev and (2012) balanced baseline Zander the from of deviation ameasure used vs. overload conditions optimal conditions), indicating a baseline as used be also can reference data assessment research. state mental (2014) Brouwer and (2015) et al. in extended discussion present an lessons of learned and pitfalls al. selection et Gerjets referenceof tasks. a careful through influences robust unrelated to ence data improved classifier’s the also Section robustness.refer the See 3.4.2 make to approach another for low affective workload different contexts showed in reference and cross-context training data that recording’s of the aspects other context. For example, (2014) et al. Mühl and high both recorded and with not interest of state the mental with neurophysiologicalcorrelate specifically that features tasks. HCI during emotions, recognition, engagement/flow/immersion)error to be used user evaluate experience can induce.they Frey (2014) et al. review (attention/vigilance/fatigue, how constructs of anumber other cognitive the to conditions states respect designs with or task interface user alternative compare and HCI tasks. more natural well as during as load tasks work experimental traditional on other hypothesis their with line in It performed recordings. later analyze to acognitive used be workload could then index basis, that constructed they this ings. On - record power band continuous EEG alpha the in parietal and theta frontal in reliable differences 1992) load levels different conditions working Arnegard of controlled found simulate three to and 2014). (Gerjets et al. tasks attention controlled in systems attentional posterior and anterior as described also sulcus, which are intraparietal the and cortex prefrontal activity, dorsolateral of EEG the interaction in the representing asymmetry alpha theta– frontal–parietal of case a reflects workload, aconsistent In finding measured. be to are that the neurophysiological on and directly thoseconditions definitions focuses of correlates conceptual methodology may able circumvents be here deliver (or approach that to adata-driven complements) objectiveintersubjective prevent and (Young differences measurement auniform 2015). et al. BCI issue ergonomics in (Wickens 2014), et al. fundamental and but aclashandconstructs of definitions Frey et al. (2016) used both a continuous EEG-based measure of workload, calibrated using an usingFrey an (2016) et al. of workload, calibrated measure acontinuous EEG-based both used those processing stimuli, of reflect to neuronal said are Evoked stimuli presented to responses the in extracted be also can features Aside continuous, measurements, frequency-based from (e.g., extreme spectrum ends of the indicating reference data underload recording than Rather exhibit ecologically and the valid are reference data recorded the that should taken be Care to, test used for be assessment example, can state mental A/B indices, Using such precalibrated For (2003) example, Smith Multi-Attribute and Task the Gevins used (Comstock Battery & could perhaps constitute a mixture of workload (compensatory actions) amixture constitute states emotional could perhaps and -back task (Kirchner 1958), and a secondary oddball task 1958), task oddball (Kirchner -back task asecondary and 73 - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 human consumption from aneurophysiological consumption from human perspective— andinvestigate methodology to BCI-based neuroscientific to have researchers turned marketing and objective definitions toIn order conceptual to values.decisions,elucidate consumer respect with is difficulty there or interviews, and gauged using questionnaires traditionally are user the to field this in improve to applied (e.g., performance al.2009). et Abootalebi be to continue algorithms Ongoing experiment. developmentsthe learning machine BCI and in of were part exposed as to participants the mock espionage that scenarios concerning information for 87.5% asystemdevised determine could accurately or not whether had they that of participants (1991) Donchin and Farwell ERPs, their question. in in on case differences Based the concerning some possess information does aclue participant as the that taken could be this ones, then neutral the to compared objects). stimuli related exhibit the to responses deviating Should participant the (e.g., information concealed the (other weapon acrime), used neutral in weapons, the others random some to related shown of stimuli, aseries are of detector. lie Participants atype knowledge are tests such, As guilty wish they conceal. to that any information possesses participant or notwhether the tests knowledge guilty basis oftion is the ERP-based 74 advantages for the validation of the measured user state. user advantages measured of for validation the the inherent has this feedback elementthe afirst ing that system.to interactivity alsosee of will We - add provide simultaneously actions participants, feedback These the to time. system real in actions for used analysis. post hoc be to later ogy, instrument, additional an as beyond technol of use itself. of goal itself the measurement the is the measurement Rather, the the any any actions, it nor not does does trigger system, influence measurement output—is missing: the ofa system abovementioned itself. and the in ­ per As definition third of the BCI systems, human’swhen the own may biased. indications be or forrequired, is example, quantification otherwise, and when a continuous recording obtained be neurophysiological use to reason of cannot interest may information when be the measurements or modelsthe may overfitted be on data available (e.g., al.2014).Haufe Babyak 2004; et Another of interest, state the to unrelated areas may variables show confounding to responses cortical up in not neurophysiologically are tions that plausible may found—for be example, neural co-varying - approaches, correla data-driven in taken: measures the validate and data resulting the interpret approaches. It however, does, to theory-driven care contradictory special and competing require provide possible can a subject-specificThis assessment. alternativeindividual, to sometimes state approach to It data-driven enables afunctional, measured. be to state the with not does interfere ing - its record and aloud). of provides activity unobtrusive data, source acontinuous, Brain potentially (e.g., thing thinking same do to the interviews, attempt introspection, that questionnaires, methods activity,using brain exhibit approaches of other to anumber these relevant compared differences state. By in mental orchanges states mental reflecting acontinuous quantification obtain to order in apply classification then and predictive basistechniques processing signal and or other their as activity of brain recording initial assessment examples an givenuse state The of mental all here r 3.2.3 experiment. the during subsequently able develop to behavior by exhibited purchase as participants index predicting an were they cortex, prefrontal mesial nucleus the the and from accumbens extracted on features Based for two attributes. product these areas cortical study, separate in activity found they discriminative decisions:chase imaging resonance aproduct’s magnetic afunctional its In price. and attractiveness of two opposing forces pur often correlates example, investigated in (2007) et al. 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Protzak following activity Shishkin on fixations and brain gaze compared for Both intended interaction. are of gazes number activations, false ahigh to lead not all as can This item (i.e., same the on one and of looks user at it time amount the for if long), that it is activated. for acertain remains cursor To the screen. if is used: time select adwell item, often or activate an follow to is used user’s the control, to for used be example, thus which gaze, can on a apointer eye an HCI, (2016) et al. tracker investigated gaze-based by HCI. In Shishkin gaze-based support to stop to moving,mand versa. vice and apassive as input com interpreted could be user,movement the in this response error elicited an continued indicating aLED When second. would upcoming the or continue stop moving within arm the (LEDs) whether user diodes the would light-emitting inform Blinking of arm. a prosthetic context. agaming in support correction error for as, HCI, (2010) example, et al. during Zander for errors correct using passive demonstrated BCI feedback. 2009) et al. (Lehne tactile 2011a) et for al. example, (Zander HCI contexts modalities, ondifferent auditory and based and robot control. have responses Error-related found in been actual also robot control, and simulated control, cursor abstract of realism: degrees but different with task asimilar essentially represent that experiments (2012) of et al. three a series in errors to Iturrate responses detectable found similar passivethe mistakes. BCI itself makes when errors in increase an to lead can this indicates, figure for one participant −6% Note that, the as 76 whereas the former is used to provide to is former used more timely the implicitwhereas executive of use passive The commands. implicitly to application’s is used control an latter such, the As we interaction. that the see mode, psychophysical or other moods as have also effect then conditions, on a more constant which will context such evoked the to states that tied or more constant them, carefully which be should then intent, or interaction perception ongoing such error as states (inter)action. transient be can These Open-loop adaptive user’s systems of ameasurement the use an to implicit parallel as input in state r 3.3.3 BCI has also been used in gaming environments to effect changes in the ongoing effect to the changes For in environments interaction. gaming in used been also BCI has (2013) et al. of cognitive by was Protzak proposed type state of use another The further and (2012) et al. by used Kreilinger been continuous also motion has the detection inform to ERN or otherwise undo to used contexts, be such can Being responses realistic reliable various, in Testing what to extent findings control would scenarios, apply HCI these realistic to more eflec t i on Brain–Computer Interfaces Handbook Interfaces Brain–Computer BCI, because the experimenters explic experimenters - the BCI, because - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 Passive Brain–Computer Interfaces Brain–Computer Passive also influences the user, next.see as influences we also shall computer.the More interactive applications exhibit closed-loop given control, where the feedback sent open-loop single,from control, result whereactions in single, detections fixed state independent closedthe next control loop influencing cycle.the executive Both actionsrepre and mode switching cycle, interaction effects do not beyond reach point: the is no current there the as atthis interactivity back cycle. given completes interaction users the the to however, This, extent of the full the is also - feed the and performed, are actions immediately, implicit input corresponding is processed The plausible. improvement implicit is input neurophysiologically the underlying any performance that taken be efficacy of the the system.in Section of As mentionedshouldmeasure still 3.2.3,though, care system. For example, compensated of errors aquantification wouldaction that not have possible input modalities. been using traditional provided be to explicitly needed inter or provides of dimension extra experiencethe to otherwise an that by user replacing interactive the commands systemsBCI these methodology either in supports tion depending on current measures of workload. such level One measures option is the range on current tion depending of automation: - opera online switch during upon by designers and them decided between the be would otherwise design decisions. adaptive system, inform An to however, of arange options that could contain order in ergonomics in research of workload Section 3.2.2, mentioned isAs ameasure important in e 3.4.2 next. described more detail, in literature example the from We asimilar see will lower to attempts load. her system the some purposefully of surgeon from relieving hertasks, the surgeon. of By the on behalf tasks certain by for assuming compensate this directly to attempt can load, it high under surgeon is currently the ofcategory asystem that applications. When detects state. mental same that influence that actions states—with changes in states—or respond certain to and online state tal tivelyconditionthe the ongoing influence of interaction. fully, well- as able as effect given qualita to monitor to of the response. each The attempts response is reached. state target desired the until user of the state the feedback manipulate to psychological system its use adaptive the is desirable, can logic then that operationalized state and user’s the motivationunambiguouslyand enjoyment.maximize is aclearly and there defined When to task of the demands the of system could type modify This improve to order ment in performance. 2008). adaptive loop The may, of positive promoteastate to for example, engage programmed be of (Csikszentmihalyi of factors, flow or astate balance state; supporting forcal example, optimally an system the influences its nextown input. is measured, that state mental the a closed By state. loop user have to is feedback thus influencing the cause achange in produced the user’s of human implement to sive input is source the method One state. ultimate mental BCI, the - system on pas based the next output influences cycle. human–machine an elementary In case of closed-loopIn systems, output is fed system words, back generated the new to other as the input. In 3.4.1 ADAPTATION CLOSED-LOOP 3.4 The examplesThe mentioned passive use here communication. BCI for implicit human-to-computer An analysis of the effects of the BCI on the interaction can also serve as a validation of the of avalidation the as serve also can interaction analysis BCI effects of of on the the the An We can again turn to our earlier example of workload in the operating theater to illustrate this this illustrate to theater example operating earlier of our workload to the in We turn again can closed-loopThus, of a men a measure adaptive systems assessment apply obtain to state mental it that is now sense system of the the interactivity in able the respond to purpose increases This motivationOne adesirable psychologi for implementation suchsustain may promoteand an to be I ntro xa mp d les u c

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t h e L i terature or correct or selections provides aclear 77 ------Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 cognitive state. short-livedtive effectuate also closed systems can feedback achieve to order loops in aone-time user, the examples in but the adap given state abovecommands, achieve to optimal a constant, aim switches mode executive between and distinction earlier the to individual). of another Similar state or “yoked” (responding random, user the to for of example, adaptation, methods preprogrammed, closed-loop adaptive system this adaptive alternative to control may compare system. to be Abetter exact is not the to behavior related experience user that in of the difference may provide inherent an theydid, condition as control,as manual a taking benchmark: an appropriate issue ofthe finding tive behaviors (i.e., more or less responsive), discuss They them. between but found difference little alevelgame’s engagement. of adap implemented They different maintain optimal to order in speed self-paced to adaptive learning. system compared as fNIRS-based the using speed and learning performance in increases Yuksel significant report et al. asufficient to degree. only when workload learned levels previous been the had step that indicated the nextline added and line of musicalonly one notes newpresented adaptive piece. first tutor The of a learning the cognitive during workload their online detect to then and pieces piano hard and activity playing easy brain between participants’ differentiate to first (fNIRS) spectroscopy infrared cognitive student. of abilities the current to catering adaptive environment, online of 73%an classifier is apply to in this aim of was word achieved Their on algebra single tasks. trials classifier, Using this properties. load task a of independent memory other ing classificationaccuracy develop to executive attempted et al. such, As aclassifier Gerjets coulddetect that resources. work on demand their to respect with differed tasks these time exercises, same atthe while learning realistic during were measured cognitive be that to same resources the targeted both tasks these as Acombination was of used, two tasks. controllable tasks calibration select to experimentally taken was care Special environments. context the present of twoment in and studies adaptive learning experience (Cowan 2014). learning management for optimal an careful load need to is said memory working overload. or information particular, In fatigue, frustration, motivation sustain to order without in inducing adjusted dynamically be can process learning the engagement the away For student. in of sustains example, the of that pacing the material tional intervention. of this because improved significantly task response on this performance mean The task. response auditory of the suspension automatic temporary the in condition workload resulted high such detection that driving an online in applied these phases later was distinguish to weretors Aclassifier present. calibrated high-workload low-workload to which phases, were compared - distrac where no phases, contextual comprehension together task). defined alistening these (a and task All calculation driving mental during (an task) interaction distractors response well as contextual auditory as human–car mimic that vehicle, the wereing. given Aside controlling tasks two participants from additional the to time. real in operator, of capacity the measured current adaptive automation Therefore, demands. task leveland capacities varying operator of fluctuating with automation varies operator, of appropriate the but the well-being performance the to and is important balance right the 1995). (Endsley &Kiris Striking out-of-the-loopthe performance problems decay of and skilled however, performance; tained negative of use with automation is associated such consequences as sus- fatigue during alleviate workload and of mental automation operator is reduce to purpose The for options). overview these an framework describing and 2000 et al. role (seesory Parasuraman autonomy supervi amere in leaving user full and the up assuming to degrees, various to operator human the operator, entirely by the or support explicit from operated be can commands a machine 78 Ewing (2016) et al. concept of closed-loop the applied Tetris, agame, to adaptation adjusting the by presented Yuksel application been has (2016): et al. Such online an near- functional used they (2014) et al. - assess Gerjets memory working online to respect with practice and review theory systems adaptive deliver to designed intelligent to applied logic tutoring be - can educa Similar highway during driv Kohlmorgen (2007), et al. measurements for EEG example, performed (Byrne & Parasuraman 1996) attempts to match the level the 1996) match to attempts the to of allocation &Parasuraman (Byrne Brain–Computer Interfaces Handbook Interfaces Brain–Computer - - - - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 (Kirchner et al. 2016). et al. (Kirchner delayedthen levels avoid to promoteadequate and of distractions engagement performance and were new instructions The was user previous occupied with the instructions. that tions indicated load, reversing logic: of current this - measures nonperception as of new these used they instruc study, another In teleoperation scenario. robotic ademanding during ceived alarms versus missed classification per rates of accurate report et al. of would perception Kirchner a state observed. be perceived. of it nonperception been Astate would new has until trigger feedbacksaliency thus until increased with alarm the system re-sound to decide the could then information, on it. on this Based act to intends user perceived the been and/or has alarm methodology or not whether assess the to is heavily asymmetrical: the human user can potentially be appraised of all details concerning the the concerning details of all appraised be potentially can user human the is heavily asymmetrical: 2001). (Fischer behavior are how model of and however, appropriate things this, In a shared HCI partner— communication world, of the the situation, and the understanding heavily on ashared however,conditions. interactive functions, specific these to limited are load. The measured a to reduction lead in automation will increased and bydemands, caused task that, enoughreasonablyto contextfor specific assume the and example, is controlled is workload abovementioned the In responses. adaptive appropriate systems, likely or causes the not their into but by states, itself, mental giveAnalysis of can activity ongoing insight momentary into brain 3.5.1 3.5 next. discussed be will method passive such at any single One time. information BCI obtain methodology can limited on the based question the how tackle to achieve systemsneed can amore complex adaptive, cooperative behavior ofof automation only have workload adaptation levels. can aone-dimensional Anext up step would simplistic logic system measure the the single of and example, loop—for the one-dimensional the achieve goals. common more closely agents, cooperating both between to order in dialogue implicit ofwe an could speak now examples we earlier communication, our saw in system. implicit Where human-to-computer the user,the user’s the user. as influence influence to feedback intend intends to The commands user, the to response informative but factnot in merely an as function for example, indicative being of of dissatisfaction. amore persistent general state given the in context as, interpreted however,would be different, can be state mental transient this if such as a define to used closed-loop be This system. necessarily cannot asecond, forlasts less than that error-related form of potential an the in state a transient For as example, perception, sured. error - is mea that state the which but to from may related different be is targeted, exact that the to state state. target the alleviate changes that and encourage allowing for changes that both bidirectional, system’s pushto for the case, former agiven the In threshold. be to may influence need potential of or interest state the to respect with equilibrium closed-loop achieveto be control can a certain of goal this The state. undesired an mitigate to or indeed states desirable mental sustain and mote pro to be used in a also can approach closed This loop. state this influence feedback, appropriate enableswith state and, systemsto mental monitor certain of a aquantification to access Direct r 3.4.3 Interfaces Brain–Computer Passive Kirchner et al. (2013) et al. Kirchner of use BCI suggest system, closed-loop making implicit alarm control of an Human–human interaction, the benchmark for natural communication and cooperation, relies cooperation, and communication for natural benchmark the interaction, Human–human amount information availableof to the by limited exemplified as cooperation is here still This does case this feedback in the because interactivity A closed feedback loop increased represents what considering is aclosed-loopWhen must given attention be system what isspecial not, and UOAE ADAPTATION AUTOMATED I ntro eflec d t u i on c t i on purposefully

the the upon acts 79 - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 could call for an additional assistant ahead of time. ahead assistant for additional an could call a systemthis, well-informed Having robot. teleoperated learned 1hwhen using aspecific after input. current abasis foras system’s the own (merely) autonomous behavior adaptations—not and system’s the user’s cognitive model, finally, generated automatically or affectiveserves that It responses. is this of the amodel aspects represent to build and gathering ment of alongside information methods other decide to act. user, autonomously system, now not interactivity: merely the and can in human also the increase additional before given, an constitutes the any new thus information known input is received. This evenuser; respondto appropriately the to ability on act to system increased A better-informed has user’s of no modelis, build the they of higher, aspects more abstract cognitive or affective behavior. user, the that do not “understand” logic—they response one-dimensional ituse for preprogrammed provide but chapter so far such information, this for in ties applications discussed its The operation. a computer understand to computer, of is the availablestate any such but information versa vice hardly (Suchman 1987). For 80 the same way as we humans learn from our own our experiences. Subsequently, from way same learn wethe as humans given context, aknown much in user this user’s understand to of the measures with computertors learns the state, mental auser’sa model in for of number fac alarge aspects life. working - By contextual and collating daily &Jatzev 2012), (Zander variables ubiquitous ahypothetical passive BCI system could such generate dissatisfaction (as, e.g., 2016), et al. of Zander relevant in recording context accurate well as an as envisioned apply to sense. a broader of, With areliable in measure for example, satisfaction versus contexts was created. different with states actions) user which were they such, correlated As observed. amodel that during contexts were of along (here: the adescription stored with measurements computer state mental or not, consequences either having direct than two examples the rather section, In given this in r 3.5.3 movements. inappropriate and of appropriate arm the inform to order in tracked were responses its observed movements error their Participants while usingprinciple arm. arobotic target. toward intended cursor the system the steer could thus go. to cursor the The wanted user where the such, as learned and, responses these behind pattern evoked movementswhile directions negative the other in system the Over responses. learned time, moved initially randomly.puter screen Movements positive to led some directions in responses, positively. exhibit to system behavior the adapted process, most perceived likely be to learning this during amodelbuilt user’s of the higher-level behaviors. presented Already the to preferences respect with &Blakeslee intelligence: It (Hawkins 2007). thus of predictive human source coding fundamental a with associated cortex, prefrontal medial the weretive in responses generated These responses. which behaviors evoked behaviors learned different exhibited and positive which evoked and nega- Tofollowed non-errors. system and the end, subjectively to that errors between distinction the make user over the what but strategy learn, time, to perceived mistakes, correct ity not immediately to (2014) et al. Zander (2016) et al. activ Zander of and error-related detections brain single-trial used e 3.5.2 Our surgeon’sOur load beyond may have levels increase to sustainable consistently been detected adaptive category of assessautomated the - applications, systems apply final state mental this In These examples were again limited to their respective environments, but the principle can be be can principle but respective the environments, their to examples limited These were again (2015) et al. earlier-mentioned asimilar Iturrate their experiments, demonstrated Extending comcontrol.on a A cursor cursor two-dimensional Specifically, approach to applied this was xa eflec mp les t i on

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t its user, it needs information and input that goes beyond necessi input that and bare its user, the information it needs h e L i terature Brain–Computer Interfaces Handbook Interfaces Brain–Computer - - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 are mitigated by the strengths of the other, while keeping these domains formally separate. formally domains other, these of keeping the while by strengths mitigated the are weaknesses their while strengths their use agents may one where get be both optimally to balance physiology their derived from are that 2014). (Fairclough From acooperative best perspective, the actions. Users goals, and have should controlprocesses, over, also ultimate ownership of, and data system’s other.each the Users should always concerning information have full to models, access two agents’ the between a balance on outcomeinfluence responsibility, strike to it aim to is prudent computer actively the in resulting state, against working undesired promotean to be used could overtechnique machine influence of man same (the harmful potentially sent, the itself remains. interaction merely the blurred, become machine, two and interactive the agents, human separating lines the As action was action, by of executed, the the whose or who, initiator authority fact, “acted.” in ultimate contexts,tive and actions it who various to may responses unclear was atsome the become point own our subjecselves. from - autonomously having learned With system this originally operating asystemwe providing what could imagine we want our before desires upon we those act can we ourselves. see an autonomous,as interactive seen be agent, mayasjust finally that intelligence may created be context, of aform the artificial and own users models of their their build to freedom the them explicitly relevant giving deemed and us. to By more to information computers giving access have others available that capable. information us, to not just we information the For all use this, long as we them as are update to continue we as models soon and as were these build to born behave to context contexts. allowscal various that We ushumans appropriately in have started systemthe itself, basis of on the acontinuouslymodel of its user. updated by automatically learned are of adaptation parameters not achieved set: the be are can which this by methods the adesirable state, mentioned sustain adaptive is, promoteand to automation, that context. that in user the most ubiquitous be to likely observed satisfy the to been computer could has execute action that that riencing high load. high riencing expe surgeon is currently the that when it theater is detected operating light could switch an on in above the Continuing example, states. awarning of detection certain the to time real respond in systems assessment, can these state Using software. of open-loop the online an adaptation inform them. surgeon operating the from demand instruments different that of resources amount the activity, generation of is the category aworkload on brain index assess to order based this in from example An hidden. remain would that otherwise may able information be and access to brated, unobtrusively, provide data). behavioral approach can data cali This individually tionnaires, be can (e.g., for measurements ameasurement, example, as activity other replace to or support brain ques- level on their based of interactivity. chapter, categories, were of four into forms divided these distinct applications. this its various in In motivation the 1973). of passive capture accurately to today, BCI appears statement research This complements and overt behavior” (Vidal transcends both that adimension in probed, be tions can Jacques- J.reac decisions and 1973, “mental Vidal. In that wrote Vidal progress, envisioning future by interfacing as brain–computer of analyses neurophysiologicaldemonstrated first real-time data, perform to ability chapter we up on the build applications have that this potential In discussed 3.6 Passive Brain–Computer Interfaces Brain–Computer Passive Given also ethical deliberations with respect to, for respect with con deliberations example, ownership,Given informed ethical data also closed-loop, system, of case alearning, adapting automatically afar-reaching,In hypothetical (i.e., models) it understanding the is the weFor part, that alarge have physi and social of our earlier- system of as the would same hypothetical the be of goal this ultimate the Although A second category of applications, open-loop adaptation, uses the obtained measurements to to measurements of obtained category applications,A second the uses open-loop adaptation, of quantification state-specific assessment, the uses of state category applications,One mental

SUMMARY AND CONCLUSION AND SUMMARY the user),the and 81 - - - - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 well, in the form of form neuroadaptivewell, technology. advanced the in show, to intends here used categorization interactivity, as the as that and, may provide ones after the user’s the with computer agency line in next intentions. providehuman–computer It in step can this this make to provide of information can asource forms, various their in systems, here discussed not did user explicitly the that executeallowing it for. itself ask and adapt to commands Passive BCI give to be to user. computer— agency the to Anext also appeared thus toward step interactivity agencyalbeit ultimate solely with experience, the human interactivewith first human–computer the options (Suchman 1987). provided interrupt and processing for This example,through, immediate quite bluntly, were control it over given not. did It humans was system, that real-time only later the or, program of processing the successful the in either resulted system This complete as programs. given beforehand and interactivity. were the to computer written early systems, In input commands development. and of light of awhole, HCI as in increased research seen history be too, The can everyday our dominate lives that (McDowell interactions 2013). et al. human–system many the alter real-world implementations adaptive of may here the systems seriously dramatically discussed and usage (e.g., 2016; et al. Krol Wei 2015; et al. 2012). Zander development these When maturity, reach its assessment, boosting state provide mental to access easy will usability universal contexts, their and applicable users across independentthe human are of that whenconstructed be classifiers can field the same.as a Finally, whole the are underlyingmay interests—but the benefit, issues since budget, and atleast effective for providing business an specific currently development infrastructure 2015;et al. 2011b, et al. Zander 2017a). is ventures commercial high-profile from interest Increased developed being improved be to are continue (e.g., and stability Goverdovsky 2016; et al. Mullen Luckily, progress. cant and easily applicable, sufficient with quality electrodes signal comfortable atsome no point longer conditions will sufficientsignifi make be to well-controlled experimental real-world developedity, and The natural, interactive properly only settings. be in researched can interactivity.toward increased error. was in prediction the open-loop that action whenan fashion, it the undo is detected for might, execute example, advance, in but do to action and what in then, intends auser that predict systems collectively neuroadaptive as 2016). et al. systems (Zander on neurophysiological based is ultimately adaptation wemation. When refer measures, these to given the process input (e.g., model) auser basis of on or updating act the infor previously learned feedback (e.g., includesthat traditional movement the of limb) aprosthetic ways other but also to we that refer It reason hidden. is for adaptation to this similarly effective passive inconspicuous, unobtrusive systems’ BCI and systems these are may responses be given respond the to to abilities shows different input. Since interactivity their categories. in Their manageable levels.levels exceed otherwise will that workload system the as predicts called may be assistant automatically additional an surgeon, For the autonomously act to system ability the the for, grants of, also user. the or on behalf This state. tal user’s of the coupled ameasure with of information, amount on alarger based adapt men and learn system single-loop The is given control: adaptation. autonomy automated the to predetermined may adaptively be theater the automated. in To surgeon’s the keep workload level, at asustainable systems operations and for example, certain states. mental detected not to merely upon the systemact but ability vides also the detect the with aclosed pro control loop. creating effect adaptation, This auser also to explicit the with purpose 82 This chapter described a trend toward increased interactivity in past and current passive current BCI and past in interactivity toward atrend increased chapter described This activ brain human on natural based investigationThe interactive systems, particular of in truly observable of the passive path emphasizes categorization here-presented BCI applicationsThe exclusive. adaptive not mutually different behaviorsThe are system adaptation automated An is neither category interactivefollowing BCI nor first three as a butbasis serves The the for Finally, control logic where the of acategory system applications of was the described exceeds is designed of category adaptation applications, closed-loop software the adaptation, afurther In Brain–Computer Interfaces Handbook Interfaces Brain–Computer , using this as a more generic term amore generic term as , using this - - - - - Downloaded By: 10.3.98.104 At: 19:51 04 Oct 2021; For: 9781351231954, chapter3, 10.1201/9781351231954-3 Frey, F. 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