Analysis and Classification of EEG Signals for Brain–Computer Interfaces PDF
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Studies 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 The series “Studies in Computational Intelligence” (SCI) publishes new develop- mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. 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More information about this series at http://www.springer.com/series/7092 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 ©SpringerNatureSwitzerlandAG2020 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. 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ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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. ©SpringerNatureSwitzerlandAG2020 3 S.Paszkiel,AnalysisandClassificationofEEGSignalsforBrain–Computer Interfaces,StudiesinComputationalIntelligence852, https://doi.org/10.1007/978-3-030-30581-9_2
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