Signal Processing and Machine Learning for Brain–Machine Interfaces

356 Pages·2018·3.19 MB·English

by  Toshihisa Tanaka

IETCONTROL,ROBOTICSANDSENSORSSERIES114
Signal Processing and
Machine Learning for
Brain--Machine Interfaces
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Signal Processing and
Machine Learning for
Brain--Machine Interfaces
Edited by
Toshihisa Tanaka and Mahnaz Arvaneh
The Institution of Engineering and Technology
PublishedbyTheInstitutionofEngineeringandTechnology,London,UnitedKingdom
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Contents
Preface xiii
1 Brain–computerinterfacesandelectroencephalogram:
basicsandpracticalissues 1
MahnazArvanehandToshihisaTanaka
Abstract 1
1.1 Introduction 1
1.2 CorecomponentsofaBMIsystem 2
1.3 Signalacquisition 3
1.3.1 Electroencephalography 4
1.3.2 Positronemissiontomography 4
1.3.3 Magnetoencephalography 4
1.3.4 Functionalmagneticresonanceimaging 4
1.3.5 Near-infraredspectroscopy 5
1.3.6 CommonlyusedmethodinBMI—whyEEG? 5
1.4 MeasurementofEEG 6
1.4.1 PrincipleofEEG 6
1.4.2 HowtomeasureEEG 6
1.4.3 Practicalissues 7
1.5 NeurophysiologicalsignalsinEEGfordrivingBMIs 9
1.5.1 Evokedpotentials 9
1.5.2 Spontaneoussignals 10
1.6 CommonlyusedEEGprocessingmethodsinBMI 11
1.6.1 Preprocessing 11
1.6.2 Re-referencing 12
1.6.3 Featureextraction 13
1.6.4 Classification 14
1.7 Feedback 14
1.8 BMIapplications 15
1.9 Summary 16
References 16
vi Signalprocessingandmachinelearningforbrain–machineinterfaces
2 Discriminativelearningofconnectivitypattern
ofmotorimageryEEG 23
XinyangLi,CuntaiGuan,andHuijuanYang
Abstract 23
2.1 Introduction 23
2.2 Discriminativelearningofconnectivitypattern
ofmotorimageryEEG 26
2.2.1 Spatialfilterdesignforvariancefeatureextraction 26
2.2.2 Discriminativelearningofconnectivitypattern 27
2.3 Experimentalstudy 28
2.3.1 Experimentalsetupanddataprocessing 28
2.3.2 Correlationresults 29
2.3.3 Classificationresults 35
2.4 Relationswithexistingmethods 36
2.5 Conclusion 36
References 37
3 AnexperimentalstudytocompareCSPandTSMtechniques
toextractfeaturesduringmotorimagerytasks 41
MatteoSartori,SimoneFiori,andToshihisaTanaka
Abstract 41
3.1 Introduction 42
3.2 Theoreticalconceptsandmethods 44
3.2.1 AveragingtechniquesofSCMs 44
3.2.2 SCMaveragesinCSPandTSMmethods 46
3.2.3 Multidimensionalscaling(MDS)algorithm 47
3.3 Experimentalresults 48
3.3.1 Classificationaccuracy 48
3.3.2 SCMsdistributionsontangentspaces 54
3.4 Conclusions 58
References 58
4 RobustEEGsignalprocessingwithsignalstructures 61
HiroshiHigashiandToshihisaTanaka
Abstract 61
4.1 Introduction 61
4.2 Sourceanalysis 63
4.3 Regularization 65
4.4 Filteringingraphspectraldomain 67
4.4.1 GraphFouriertransform 67
4.4.2 SmoothinganddimensionalityreductionbyGFT 70
4.4.3 TangentspacemappingfromRiemannianmanifold 72
4.4.4 Smoothingonfunctionalbrainstructures 74
4.5 Conclusion 76
References 76
Contents vii
5 Areviewontransferlearningapproachesinbrain–computer
interface 81
AhmedM.Azab,JakeToth,LyudmilaS.Mihaylova,
andMahnazArvaneh
Abstract 81
5.1 Introduction 81
5.2 Transferlearning 82
5.2.1 Historyoftransferlearning 82
5.2.2 Transferlearningdefinition 83
5.2.3 Transferlearningcategories 84
5.3 Transferlearningapproaches 85
5.3.1 Instance-basedtransferlearning 85
5.3.2 Feature-representationtransferlearning 85
5.3.3 Classifier-basedtransferlearning 85
5.3.4 Relational-basedtransferlearning 86
5.4 TransferlearningmethodsusedinBCI 86
5.4.1 Instance-basedtransferlearninginBCI 86
5.4.2 Feature-representationtransferlearning
inBCI 88
5.4.3 Classifier-basedtransferlearninginBCI 92
5.4.4 Unsupervisedtransferlearning 96
5.5 Challengesanddiscussion 96
5.5.1 Instance-basedtransferlearninginBCI 96
5.5.2 Feature-representationtransferlearninginBCI 97
5.5.3 Classifier-basedtransferlearninginBCI 97
5.6 Summary 97
References 98
6 Unsupervisedlearningforbrain–computerinterfacesbasedon
event-relatedpotentials 103
Pieter-JanKindermans,DavidHübner,ThibaultVerhoeven,
Klaus-RobertMüller,andMichaelTangermann
Abstract 103
6.1 Introduction 103
6.2 Event-relatedpotentialbasedbrain–computer
interfaces 106
6.3 Decodingbasedonexpectationmaximisation 107
6.3.1 TheprobabilisticmodelforERPBCI 107
6.3.2 Trainingthemodel 108
6.4 Decodingbasedonlearningfromlabelproportions 110
6.4.1 Learningfromlabelproportions 110
6.4.2 AmodifiedERPparadigm 111
6.4.3 TrainingoftheLLPmodel 112
6.5 CombiningEMandLLPdecodersanalytically 113
6.5.1 TrainingtheMIXmodel 114
viii Signalprocessingandmachinelearningforbrain–machineinterfaces
6.6 Experimentalsetup 114
6.6.1 Data 114
6.6.2 Dataprocessing 115
6.6.3 Methodsandhyperparameters 115
6.7 Results 116
6.8 Conclusion 120
Acknowledgements 120
References 121
7 Covariateshiftdetection-basednonstationaryadaptation
inmotor-imagery-basedbrain–computerinterface 125
HaiderRazaandDheerajRathee
Abstract 125
7.1 Introduction 125
7.2 Background 127
7.2.1 CovariateshiftinEEGsignals 127
7.2.2 AdaptivelearningmethodsinEEG-basedBCI 127
7.3 Covariateshiftdetection-basednonstationaryadaptation
(CSD-NSA)algorithm 129
7.3.1 Problemformulation 129
7.3.2 Covariateshiftdetection(CSD)test 130
7.3.3 SupervisedCSD–NSAalgorithm 131
7.3.4 UnsupervisedCSD-NSAalgorithm 132
7.4 ExperimentalvalidationoftheCSD-NSAalgorithms 135
7.4.1 EEGdataset 135
7.4.2 Signalprocessingandfeatureextraction 135
7.4.3 Featureselectionandparameterestimation 136
7.4.4 Empiricalresults 137
7.5 Discussionandfutureprospects 138
References 139
8 ABCIchallengeforthesignal-processingcommunity:
consideringtheuserintheloop 143
FabienLotte,CamilleJeunet,JelenaMladenovic,
BernardN’Kaoua,andLéaPillette
Abstract 143
8.1 Introduction 144
8.2 Modelingtheuser 145
8.2.1 Estimatingandtrackingtheuser’smentalstatesfrom
multimodalsensors 146
8.2.2 Quantifyingusers’skills 148
8.2.3 Creatingadynamicmodeloftheusers’states
andskills 149
Contents ix
8.3 ImprovingBCIusertraining 155
8.3.1 Designingfeaturesandclassifiersthattheusercan
understandandlearnfrom 156
8.3.2 Identifyingwhentoupdateclassifierstoenhance
learning 157
8.3.3 DesigningBCIfeedbacksensuringlearning 158
8.4 Conclusion 163
Acknowledgments 164
References 164
9 Feedforwardartificialneuralnetworksforevent-related
potentialdetection 173
HubertCecotti
Abstract 173
9.1 Introduction 173
9.2 Event-relatedpotentials 175
9.3 Feedforwardneuralnetworks 176
9.3.1 Activationfunctions 177
9.3.2 Errorevaluation 178
9.3.3 Architectures 178
9.4 Methods 183
9.5 Experimentalprotocol 184
9.5.1 Convnets 184
9.5.2 Performanceevaluation 186
9.6 Results 186
9.7 Discussion 188
9.8 Conclusion 190
References 190
10 Signalmodelsforbraininterfacesbasedonevokedresponse
potentialinEEG 193
YeganehM.Marghi,PaulaGonzalez-Navarro,FernandoQuivira,
JamesMcLean,BrunaGirvent,MohammadMoghadamfalahi,
MuratAkcakaya,andDenizErdogmus
Abstract 193
10.1 ERP-basedBCIs 193
10.1.1 MultidimensionalEEGclassification 195
10.1.2 NonstationaritiesinEEGsignals 195
10.1.3 Noiseintheclasslabels 196
10.2 ERP-basedinference 197
10.2.1 ERPdetection 197
10.2.2 Linearmodelandcovariancematrixstructures 198
10.2.3 Nonstationaritiesdetection 203
10.2.4 DecouplingtheclasslabelfromERPdetection 205