Table Of ContentStudies in Computational Intelligence 852
Szczepan Paszkiel
Analysis and
Classification of
EEG Signals for
Brain–Computer
Interfaces
Studies in Computational Intelligence
Volume 852
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
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Szczepan Paszkiel
fi
Analysis and Classi cation
–
of EEG Signals for Brain
Computer Interfaces
123
Szczepan Paszkiel
Department ofBiomedical Engineering,
FacultyofElectricalEngineering,Automatic
Control andInformatics
OpoleUniversity of Technology
Opole, Poland
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-030-30580-2 ISBN978-3-030-30581-9 (eBook)
https://doi.org/10.1007/978-3-030-30581-9
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Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Data Acquisition Methods for Human Brain Activity. . . . . . . . . . . 3
2.1 Electroencephalography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.1 EEG Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 Artifacts in EEG Signal . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Magnetoencelography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Functional Magnetic Resonance Imaging . . . . . . . . . . . . . . . . . . 7
2.4 Positron Emission Tomography. . . . . . . . . . . . . . . . . . . . . . . . . 8
2.5 Near Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Brain–Computer Interface Technology . . . . . . . . . . . . . . . . . . . . . . 11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Using the Moore-Penrose Pseudoinverse for the EEG Signal
Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5 Using the LORETA Method for Localization of the EEG Signal
Sources in BCI Technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
6 Data Analysis of Human Brain Activity Using MATLAB
Environment with EEGLAB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
7 Using Neural Networks for Classification of the Changes
in the EEG Signal Based on Facial Expressions . . . . . . . . . . . . . . . 41
7.1 Machine Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
7.2 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
v
vi Contents
7.3 Neural Network Implementation . . . . . . . . . . . . . . . . . . . . . . . . 49
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
8 Using BCI Technology for Controlling a Mobile Vehicle . . . . . . . . 71
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
9 Using BCI Technology for Controlling a Mobile Vehicle
Using LabVIEW Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
10 Augmented Reality (AR) Technology in Correlation
with Brain–Computer Interface Technology . . . . . . . . . . . . . . . . . . 87
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
11 Using BCI and VR Technology in Neurogaming . . . . . . . . . . . . . . 93
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
12 Computer Game in UNITY Environment for BCI Technology. . . . 101
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
13 Using BCI in IoT Implementation. . . . . . . . . . . . . . . . . . . . . . . . . . 111
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
14 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Appendix. .... .... .... .... ..... .... .... .... .... .... ..... .... 131
Chapter 1
Introduction
Thismonographisacollectionofinformationonthedevelopmentofbrain–computer
(BCI) technology with particular focus on data acquisition methods-tools used for
humanbrainactivity.Duetouniversalandeasyapplication,authorfocusedontheuse
ofelectroencephalographyasanessentialmethodcommonlyusedinmeasurements
fortheneedsofthedevelopmentofBCItechnology.Thismethodmakesitpossible
to investigate bioelectric activity of neurons by placing electrodes directly on the
corticalsurface(invasivemethod)orontheheadsurface(non-invasivemethod).The
signal received during such implementations is an electroencephalographic signal,
inwhichbrainwavesoscillationssuchas:alpha,beta,theta,gamma,lambdaband
oscillations etc., are separated. However, electroencephalography is not the only
method of acquisition used during the realization of solutions within the brain—
computerinterfacetechnology,therefore,themethodsofhumanbraininvestigations
suchas:Magnetoencephalography(MEG),FunctionalMagneticResonanceImag-
ing (FMRI), Positron Emission Tomography (PET), Near Infrared Spectroscopy
(NIRS)arediscussedinthismonograph.Themethodsofdataanalysisinthescope
ofhumanbrainactivity,including,amongothers,statisticalmethodsaredescribed
inthefollowingchapters.Also,theMoore-Penrosepseudoinverseasapotentialtool
fortheEEGsignalreconstructionispresented.Furthermore,theuseoftheLORETA
methodforlocalizationoftheEEGsignalsourcesinBCItechnologyisdiscussed;it
isthemethodforbrainactivityimagingbasedonelectroencelographicandmagne-
toencephalographicrecords.Themonographalsodiscussestheissueofusingneural
networksforclassificationofthechangesintheEEGsignalbasedonfacialexpres-
sions,whichwasthenimplementedinpracticalimplementationsofthedevelopments
basedontheresearchresults.
Theimplementationpartofthemonographreferstotheauthor’suseofBCItech-
nologyincontrolprocesses.Anideaofcontrollingamobilevehiclebasedonfacial
expressions,whichgeneratinganartifactintheEEGsignaladequatetotheperfor-
manceofagivenactivity,wasclassifiedfortheneedsoftheprocessofcontrolling
amobilerobot.Anotherexampleofpracticalimplementationreferstotheoriginal
©SpringerNatureSwitzerlandAG2020 1
S.Paszkiel,AnalysisandClassificationofEEGSignalsforBrain–Computer
Interfaces,StudiesinComputationalIntelligence852,
https://doi.org/10.1007/978-3-030-30581-9_1
2 1 Introduction
use,inthescopeofrealizationmethodsofcontrolinBCItechnology,ofLabVIEW
environment.
A dynamically developing Virtual Reality (VR) and Augmented Reality (AR)
technology has become an impetus for developing the concept of combining AR
withBCItechnologyandthentheapplicationofVRtechnologyincorrelationwith
BCItechnology.Withintheresearchwork,alsoanexemplaryvideogameinUNITY
environmentwasdevelopedwhichmaybesuccessfullyusedinawidelydeveloped
neurogamingbasingonBCItechnology,whichisdescribedinoneofthesubsequent
chapters.
Within the developments being the outcome of the research work on the
brain–computertechnology,includingidentificationofthesourcesofthebrainsig-
nals generation due to correlation of neuronal cell fractions [1], the possibility of
implementation of the solutions coming from BCI technology in the scope of the
popularIoTtechnologyintheaspectofsmarthomesisalsopresented.
The monograph ends with a chapter summing up the obtained results of the
researchworks,withparticularfocusontheirapplicationpossibilitiesintheaspect
ofcarryingoutdevelopmentsinbrain–computertechnology.
Reference
1. Accardo,A.,Affinito,M.,Carrozzi,M.,Bouquet,F.:Useofthefractaldimensionfortheanalysis
ofelectroencephalographictimeseries.Biol.Cybern.77,339–350(1997)
Chapter 2
Data Acquisition Methods for Human
Brain Activity
2.1 Electroencephalography
Clinicalelectroencephalographyisoneofseveralmethodsofdataacquisitionfrom
humanbrain.ItwasintroducedbyHansBerger,aGermanpsychiatristinthe1930s
[3].Itisanoninvasivemethodconsistingindetectionandregistrationofelectrical
activityofthebrainusingelectrodesattachedtothescalpwhichregisterchangesof
electric potential on the skin surface coming from the activity of cerebral neurons
[2]andaftertheiramplificationtheyformarecord—anencephalogram.Thevalue
ofthepotentialregisteredbyconsecutiveelectrodescanbedescribedbyEq.(2.1).
V = V +V (2.1)
n EEGn CMS
where:V —potentialvalueonelectrodes,V —potentialconnectedwithelectri-
n EEGn
cal activity of the brain. V —common signal on all electrodes, also connected
CMS
withinterferencefromthenetwork.
Presently,EEGismostoftenusedbyneurologiststodifferentiatefunctionalfrom
organicbraindiseases,todiagnosesleepdisorders,headaches,dizziness,tomonitor
brainactivityduringheartoperations.EEGoffershightemporalresolutionwhichis
not possible with MRI. Obtaining electrode resistance not exceeding 10k (cid:2) at the
startoftheinvestigationisanessentialconditionforobtainingagoodqualityEEG.
It depends, to a large extent, on proper preparation of the scalp, which before the
electrodesareattached,shouldbecarefullydegreasedandthesuperficialcalloused
layeroftheepidermisremoved.
Thedisadvantageofusinganelectroencephalographinpracticeis,amongothers,
limitationofresolutionbyequipmentcapabilitiesandtheneedtouseacomputerto
viewandanalyzedata.
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S.Paszkiel,AnalysisandClassificationofEEGSignalsforBrain–Computer
Interfaces,StudiesinComputationalIntelligence852,
https://doi.org/10.1007/978-3-030-30581-9_2
