Electroencephalography (EEG) Technology Applications and Available Devices
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: the Linear Tool Kit
bioRxiv preprint doi: https://doi.org/10.1101/672956; this version posted June 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Towards an Objective Evaluation of EEG/MEG Source Estimation Methods: The Linear Tool Kit Olaf Hauk1, Matti Stenroos2, Matthias Treder3 1 MRC Cognition and Brain Sciences Unit, University of Cambridge, UK 2 Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland 3 School of Computer Sciences and Informatics, Cardiff University, Cardiff, UK Corresponding author: Olaf Hauk MRC Cognition and Brain Sciences Unit University of Cambridge 15 Chaucer Road Cambridge CB2 7EF [email protected] Key words: inverse problem, point-spread function, cross-talk function, resolution matrix, minimum-norm estimation, spatial filter, beamforming, localization error, spatial dispersion, relative amplitude 1 bioRxiv preprint doi: https://doi.org/10.1101/672956; this version posted June 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract The question “What is the spatial resolution of EEG/MEG?” can only be answered with many ifs and buts, as the answer depends on a large number of parameters. Here, we describe a framework for resolution analysis of EEG/MEG source estimation, focusing on linear methods. -
Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals
sensors Article Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals Dong-Woo Koh 1, Jin-Kook Kwon 2 and Sang-Goog Lee 1,* 1 Department of Media Engineering, Catholic University of Korea, 43 Jibong-ro, Bucheon-si 14662, Korea; [email protected] 2 CookingMind Cop. 23 Seocho-daero 74-gil, Seocho-gu, Seoul 06621, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-2-2164-4909 Abstract: Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. -
10 Dangerous Drugs That Attack Your Brain and Body (PDF)
ALCOHOL Legal but dangerous. Causes changes in behavior, impairs judgement and coordination required to drive a car safely. Moderate to high doses severely alters a person's ability to learn and remember. Very high doses cause death. 10 Dangerous Drugs: Long term consumption of large quantities, combined with poor nutrition, can lead to permanent damage to brain. liver, pancreas and stomach. That attack your Brain and AMPHETAMINES Body Illegal. Users may experience sweating, headaches, blurred vision, dizziness, sleeplessness and anxiety. High doses can cause irregular heartbeat, loss of coordination, and collapse. Injection creates a sudden increase in blood pressure that can result in very high fever, or heart failure and death. BARBITURATES Illegal. Effects are similar of those of alcohol: slurred speech, staggering walk. Very large doses can cause coma and death. Combination of barbiturates and alcohol multiplies risks. COCAINE Illegal. Stimulates the central nervous system. Immediate effects include dilated pupils, elevated blood pressure, heart and respiratory problems, and stuffy nose. Crack, or freebase rock, is extremely addictive, and effects are felt within 10 seconds! Causes dilated pupils, increased pulse rate, elevated blood pressure, insomnia, loss of appetite, hallucinations, paranoia, and seizures. Use of cocaine can cause death by cardiac arrest. HALLUCINOGENS Lysergic acid. (LSD) Commonly causes sensations and feelings to change rapidly. Causes illusions (being deceived by the normal) and hallucinations (a false or mistaken idea) dilated pupils, elevated body temperature, increased heart rate and blood pressure, loss of appetite, sleeplessness and tremors. The user may experience panic, confusion, suspicions, anxiety and loss of control. Delayed effects (flashbacks), can occur even after use has ceased. -
Analysis of EEG Signal Processing Techniques Based on Spectrograms
ISSN 1870-4069 Analysis of EEG Signal Processing Techniques based on Spectrograms Ricardo Ramos-Aguilar, J. Arturo Olvera-López, Ivan Olmos-Pineda Benemérita Universidad Autónoma de Puebla, Faculty of Computer Science, Puebla, Mexico [email protected],{aolvera, iolmos}@cs.buap.mx Abstract. Current approaches for the processing and analysis of EEG signals consist mainly of three phases: preprocessing, feature extraction, and classifica- tion. The analysis of EEG signals is through different domains: time, frequency, or time-frequency; the former is the most common, while the latter shows com- petitive results, implementing different techniques with several advantages in analysis. This paper aims to present a general description of works and method- ologies of EEG signal analysis in time-frequency, using Short Time Fourier Transform (STFT) as a representation or a spectrogram to analyze EEG signals. Keywords. EEG signals, spectrogram, short time Fourier transform. 1 Introduction The human brain is one of the most complex organs in the human body. It is considered the center of the human nervous system and controls different organs and functions, such as the pumping of the heart, the secretion of glands, breathing, and internal tem- perature. Cells called neurons are the basic units of the brain, which send electrical signals to control the human body and can be measured using Electroencephalography (EEG). Electroencephalography measures the electrical activity of the brain by recording via electrodes placed either on the scalp or on the cortex. These time-varying records produce potential differences because of the electrical cerebral activity. The signal generated by this electrical activity is a complex random signal and is non-stationary [1]. -
Optogenetic Investigation of Neuronal Excitability and Sensory
OPTOGENETIC INVESTIGATION OF NEURONAL EXCITABILITY AND SENSORY- MOTOR FUNCTION FOLLOWING A TRANSIENT GLOBAL ISCHEMIA IN MICE by Yicheng Xie Msc, The University of Missouri-Columbia, 2011 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Neuroscience) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2015 © Yicheng Xie, 2015 Abstract Global ischemia occurs during cardiac arrest and has been implicated as a complication that can occur during cardiac surgery. It induces delayed neuronal death in human and animal models, particularly in the hippocampus, while it also can affect the cortex. Other than morphology and measures of cell death, relatively few studies have examined neuronal networks and motor- sensory function following reversible global ischemia in vivo. Optogenetics allows the combination of genetics and optics to control or monitor cells in living tissues. Here, I adapted optogenetics to examine neuronal excitability and motor function in the mouse cortex following a transient global ischemia. Following optogenetic stimulation, I recorded electrical signals from direct stimulation to targeted neuronal populations before and after a 5 min transient global ischemia. I found that both excitatory and inhibitory neuronal network in the somatosensory cortex exhibited prolonged suppression of synaptic transmission despite reperfusion, while the excitability and morphology of neurons recovered rapidly and more completely. Next, I adapted optogenetic motor mapping to investigate the changes of motor processing, and compared to the changes of sensory processing following the transient global ischemia. I found that both sensory and motor processing showed prolonged impairments despite of the recovery of neuronal excitability following reperfusion, presumably due to the unrestored synaptic transmission. -
Analysis of EEG Signals for EEG-Based Brain-Computer Interface
Analysis of EEG Signals For EEG-based Brain-Computer Interface Jessy Parokaran Varghese School of Innovation, Design and Technology Mälardalen University Vasteras, Sweden Supervisor and Examiner: Dr. BARAN ÇÜRÜKLÜ Date: July 2009 Abstract Advancements in biomedical signal processing techniques have led Electroencephalography (EEG) signals to be more widely used in the diagnosis of brain diseases and in the field of Brain Computer Interface(BCI). BCI is an interfacing system that uses electrical signals from the brain (eg: EEG) as an input to control other devices such as a computer, wheel chair, robotic arm etc. The aim of this work is to analyse the EEG data to see how humans can control machines using their thoughts.In this thesis the reactivity of EEG rhythms in association with normal, voluntary and imagery of hand movements were studied using EEGLAB, a signal processing toolbox under MATLAB. In awake people, primary sensory or motor cortical areas often display 8-12 Hz EEG activity called ’Mu’ rhythm ,when they are not engaged in processing sensory input or produce motor output. Movement or preparation of movement is typically accompanied by a decrease in this mu rhythm called ’event-related desynchronization’(ERD). Four males, three right handed and one left handed participated in this study. There were two sessions for each subject and three possible types : Imagery, Voluntary and Normal. The EEG data was sampled at 256Hz , band pass filtered between 0.1 Hz and 50 Hz and then epochs of four events : Left button press , Right button press, Right arrow ,Left arrow were extracted followed by baseline removal.After this preprocessing of EEG data, the epoch files were studied by analysing Event Related Potential plots, Independent Component Analysis, Power spectral Analysis and Time- Frequency plots. -
Neuro Anesthesia for Trauma Patients
Neuro Anesthesia for Traumatic Brain Injury A Review of The Basics Michele Kolowitz CRNA, MHS October 2014 Introduction CDC • 1.7 Million sustained TBI • 52,000 Deaths • 275,000 Hospitalizations • 80% treated & released from ER (~1.3 million) • Those ≥ 75 highest rates of TBI-related hospitalization & death The Brain Trauma Foundation • TBI affects 2% of the population annually • Major cause of death & severe disability among young people • Most important complication—intracranial hematoma CDC 2002-2006 The BTF 2007 Objectives • Review Cerebral Anatomy, Physiology, & Circulation • Explore The 2007 Brain Trauma Foundation Guidelines for Management of Severe TBI • Discuss Sodium & Water Balance after TBI • Central Neurogenic Diabetes Insipidus • Syndrome of Inappropriate Secretion of Antidiuretic Hormone • Cerebral Salt-Wasting Syndrome Cerebral Anatomy Cranial Vault Brain 80% Blood 12% CSF 8% Brain 1300 grams (3lbs) ~20% Cardiac Output High metabolic rate Absence of O2 stores Cerebral Metabolism—CMRO2 Oxygen Consumption 3-3.8mL/100g/min Average adult ~50ml/min 60% generate ATP neuronal electrical activity *ABSENCE of significant O2 reserves when O2 tension <30 mm/Hg 3-8 min before ATP depletedirreversible cellular injury Cerebral Metabolism & Glucose Glucose 5mg/100g/min Average Adult ~65-70mg/min • 90% aerobic metabolism • CMRO2 parallels glucose consumption • Can metabolize some lactate *Acute Sustained HYPOglycemia is equally as devastating as hypoxia Aerobic vs. Anaerobic Metabolism Cerebral Blood Flow Normal CBF 40-50ml/100g/min Average adult ~750 ml/min • Global BF & metabolic rate remain fairly stable • Regional BF & metabolic rate can change dramatically As metabolic rate goes up, BF goes up—Coupling Increase [K+ & H+] in ECF arteriole dilation & BF Barash 2006 Manipulating CO2 CO2 causes vasodilation & Blood Flow CO2 from 40 to 80 mm/Hg—DOUBLE BF CO2 from 40 to 20 mm/Hg—HALVES BF *Changes are transcient lasting ~6-8hours. -
Proquest Dissertations
INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMi films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6” x 9” black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. UMI Bell & Howell Information and Learning 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 800-521-0600 MULTIMODALITY ELECTROPHYSIOLOGICAL MONITORING IN THE NEUROINTENSIVE GARE UNIT DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By André van der Kouwe, M.Eng. -
Analysis and Classification of EEG Signals Using Probabilistic Models
Analysis and Classification of EEG Signals using Probabilistic Models for Brain Computer Interfaces THESE` No 3547 (2006) present´ee `ala Facult´esciences et techniques de l’ing´enieur Ecole´ Polytechnique F´ed´erale de Lausanne pour l’obtention du grade de docteur `es sciences par Silvia Chiappa Laurea Degree in Mathematics, University of Bologna, Italy accept´ee sur proposition du jury: Prof. Jos´edel R. Mill´an, directeur de th`ese Dr. Samy Bengio, co-directeur de th`ese Dr. David Barber, rapporteur Dr. Sara Gonzalez Andino, rapporteur Prof. Klaus-Robert M¨uller, rapporteur Lausanne, EPFL 2006 2 Riassunto Questa tesi esplora l’utilizzo di modelli probabilistici a variabili nascoste per l’analisi e la clas- sificazione dei segnali elettroencefalografici (EEG) usati in sistemi Brain Computer Interface (BCI). La prima parte della tesi esplora l’utilizzo di modelli probabilistici per la classificazione. Iniziamo con l’analizzare la differenza tra modelli generativi e discriminativi. Allo scopo di tenere in considerazione la natura temporale del segnale EEG, utilizziamo due modelli dinamici: il modello generativo hidden Markov model e il modello discriminativo input-output hidden Markov model. Per quest’ultimo modello, introduciamo un nuovo algoritmo di apprendimento che `edi particolare beneficio per il tipo di sequenze EEG utilizzate. Analizziamo inoltre il vantaggio nell’utilizzare questi modelli dinamici verso i loro equivalenti statici. In seguito, analizziamo l’introduzione di informazione pi`uspecifica circa la struttura del seg- nale EEG. In particolare, un’assunzione comune nell’ambito di ricerca relativa al segnale EEG `eil fatto che il segnale sia generato da una trasformazione lineare di sorgenti indipendenti nel cervello e altre componenti esterne. -
Multiple Mechanisms Link Prestimulus Neural Oscillations to Sensory Responses
RESEARCH ARTICLE Multiple mechanisms link prestimulus neural oscillations to sensory responses Luca Iemi1,2,3*, Niko A Busch4,5, Annamaria Laudini6, Saskia Haegens1,7, Jason Samaha8, Arno Villringer2,6, Vadim V Nikulin2,3,9,10* 1Department of Neurological Surgery, Columbia University College of Physicians and Surgeons, New York City, United States; 2Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; 3Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russian Federation; 4Institute of Psychology, University of Mu¨ nster, Mu¨ nster, Germany; 5Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Mu¨ nster, Mu¨ nster, Germany; 6Berlin School of Mind and Brain, Humboldt- Universita¨ t zu Berlin, Berlin, Germany; 7Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; 8Department of Psychology, University of California, Santa Cruz, Santa Cruz, United States; 9Department of Neurology, Charite´-Universita¨ tsmedizin Berlin, Berlin, Germany; 10Bernstein Center for Computational Neuroscience, Berlin, Germany Abstract Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an *For correspondence: amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic [email protected] (LI); signal and sleepiness. -
EEG Correlates of Old/New Discrimination Performance Involving Abstract Figures and Non-Words
brain sciences Article EEG Correlates of Old/New Discrimination Performance Involving Abstract Figures and Non-Words Monika Toth * , Anke Sambeth and Arjan Blokland Department of Neuropsychology and Psychopharmacology, Maastricht University, 6229 ER Maastricht, The Netherlands; [email protected] (A.S.); [email protected] (A.B.) * Correspondence: [email protected] Abstract: The processing of pre-experimentally unfamiliar stimuli such as abstract figures and non- words is poorly understood. Here, we considered the role of memory strength in the discrimination process of such stimuli using a three-phase old/new recognition memory paradigm. Memory strength was manipulated as a function of the levels of processing (deep vs. shallow) and repetition. Behavioral results were matched to brain responses using EEG. We found that correct identification of the new abstract figures and non-words was superior to old item recognition when they were merely studied without repetition, but not when they were semantically processed or drawn. EEG results indicated that successful new item identification was marked by a combination of the absence of familiarity (N400) and recollection (P600) for the studied figures. For both the abstract figures and the non-words, the parietal P600 was found to differentiate between the old and new items (late old/new effects). The present study extends current knowledge on the processing of pre-experimentally unfamiliar figurative and verbal stimuli by showing that their discrimination depends on experimentally induced memory strength and that the underlying brain processes differ. Nevertheless, the P600, Citation: Toth, M.; Sambeth, A.; similar to pre-experimentally familiar figures and words, likely reflects improved recognition memory Blokland, A. -
Non Invasive Brain-Machine Interfaces Final Report
Non Invasive Brain-Machine Interfaces Final Report Authors: José del R. Millán, Pierre W. Ferrez, Anna Buttfield Affiliation: IDIAP Research Institute ESA Research Fellow/Technical Officer: Carlo Menon Contacts: Prof. Dr. José del R. Millán Tel: +41-27-7217770 Fax: +41-27-7217712 e-mail: [email protected] Dr. Carlo Menon Tel: +31(0)715658675 Fax: +31(0)715658018 e-mail: [email protected] Ariadna ID: 05/6402 Available on the ACT website Study Duration: 2 months http://www.esa.int/act Contract Number: 19708/06/NL/HE Table of Contents 1. Introduction............................................................................................. 3 2. EEG-based Brain-Computer Interfaces: Methodologies ........................ 6 2.1 Evoked BCIs ................................................................................... 6 2.2 Spontaneous BCI............................................................................. 7 2.2.1 Slow Potentials................................................................. 8 2.2.2 Rhythmic Activity............................................................ 9 2.3 Operant Conditioning & Machine Learning ................................. 11 2.4 Synchronous vs. Asynchronous BCI............................................. 12 2.5 Spatial Filtering............................................................................. 13 3. Hardware............................................................................................... 14 4. Current Applications............................................................................