Artifact Analysis and Removal of Electroencephalographic (EEG)

93 Pages·2017·3.44 MB·English

Artifact Analysis and Removal of
Electroencephalographic (EEG) Recordings
YiDou
AThesis
in
TheDepartment
of
ConcordiaInstituteforInformationSystemsEngineering
PresentedinPartialFulfillmentoftheRequirements
fortheDegreeof
MasterofAppliedScience(QualitySystemEngineering)at
ConcordiaUniversity
Montre´al,Que´bec,Canada
March2017
(cid:13)c YiDou,2017
CONCORDIA UNIVERSITY
School of Graduate Studies
Thisistocertifythatthethesisprepared
By: YiDou
Entitled: Artifact Analysis and Removal of Electroencephalographic
(EEG)Recordings
andsubmittedinpartialfulfillmentoftherequirementsforthedegreeof
MasterofAppliedScience(QualitySystemEngineering)
complies with the regulations of this University and meets the accepted standards with re-
specttooriginalityandquality.
SignedbytheFinalExaminingCommittee:
Chair
Dr. JiaYuanYu
ExternalExaminer
Dr. ZhiChen(BCEE)
Examiner
Dr. JiaYuanYu
Supervisor
Dr. YongZeng
Co-supervisor
Dr. Wei-PingZhu(ECE)
Approvedby
RachidaDssouli,Chair
Department of Concordia Institute for Information Systems
Engineering
2017
AmirAsif,Dean
FacultyofEngineeringandComputerScience
Abstract
Artifact Analysis and Removal of Electroencephalographic (EEG)
Recordings
YiDou
Electroencephalography (EEG) technique has been widely used in continuous mon-
itoring the brain activities in academic research and clinical applications. In cognitive
neuroscience research, the electrical brain signals can be used to measure mental effort of
subjects. However, the presence of artifacts is a constant problem when recording brain
activities, which will obscure the underlying neural dynamics and therefore make it diffi-
cult to interpret EEG signals accurately. These unwanted signals or artifacts have different
effects depending on their sources of generation. Among them, the motion of the subject
is one of the major contributors to physiological artifacts that causes most of the contam-
inations to the underlying brain activities. It is quite challenging to correct the myogenic
activity from EEG background potentials due to its wide spectral distribution overlapped
withtypicalbandsofbrainwavesrelatedtocognitiveactivities,andthespatialdistribution
over the entire scalp of human. As such, this thesis focuses on the analysis and removal of
motionartifactsfromEEGsignals.
The preliminary investigations include the movement-triggered artifact identification
and the analysis of the characteristics of the motion artifact. According to the recorded
video, the contaminated epochs are extracted from the original EEG signals. A set of fea-
tures of the movement-triggered artifacts are proposed based on power spectral density
and wavelet transform. Statistical analysis is performed to distinguish the segments that
iv
contain motions. Two typical methods of artifact removal are then studied, and the effi-
ciency to correct this type of artifact is validated by comparing the extracted features of
non-movement segments and the contaminated segments. The result shows that the tested
artifact removal methods cannot completely remove movement artifacts, which also infers
thepotentialrelationbetweenmotionandmentalactivities.
v
Acknowledgments
Firstandforemost,Iwouldliketoexpressmyprofoundappreciationtomysupervisors
Dr. Yong Zeng and Dr. Wei-Ping Zhu, for their constant guidance and support throughout
the course of my graduate study and research. It would not be possible to complete this
thesiswithouttheirinsightfuladviceandinspiration. Itisagreatfortunetohavetheoppor-
tunitytoworkwithtwosuchoutstandingprofessors. Iamimpressedandmotivatedbytheir
enthusiasmfortheresearch,andIbenefitedgreatlyfromtheirknowledgeandwisdom.
I would also like to thank all my colleagues in the Design Lab. It is very fortunate to
beinthisfriendlyresearchenvironmentandcollaboratewithallthepastandpresentgroup
members. Special thanks to Dr. Thanh An Nguyen, for her help and valuable advice in
EEG research. I gratefully acknowledge all the friends that made my time at Concordia
enjoyable.
Last but not the least, I would like to thank my beloved parents, for providing me with
thecontinuousencouragementandunconditionalloveinmylife. Thankyou.
vi
Contents
ListofFigures ix
ListofTables xi
1 Introduction 1
1.1 BackgroundandMotivation . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Thesisorganization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 BackgroundMaterial 6
2.1 Electroencephalography(EEG) . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 Lobesofthebrain . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Brainwaves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.3 Event-relatedpotentials(ERPs) . . . . . . . . . . . . . . . . . . . 10
2.2 Artifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Typicalartifacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Movement-triggeredartifact . . . . . . . . . . . . . . . . . . . . . 16
3 SignalCollectionandExperimentalSetup 18
3.1 Selectionofparticipants . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
vii
3.2 EEGsignalrecording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3 Movementrecording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 Experimentprocedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.5 Datapre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Movement-triggeredArtifactAnalysis 26
4.1 EEGdatasegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 Featureextractioninartifactanalysis . . . . . . . . . . . . . . . . . . . . . 31
4.2.1 Powerspectralanalysis . . . . . . . . . . . . . . . . . . . . . . . . 32
4.2.2 Waveletanalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3 Experimentalresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.3.1 ResultsusingrelativeBeta2power . . . . . . . . . . . . . . . . . . 40
4.3.2 Resultsusingwaveletentropy . . . . . . . . . . . . . . . . . . . . 46
5 ArtifactRemoval 49
5.1 Canonicalcorrelationanalysis(CCA) . . . . . . . . . . . . . . . . . . . . 50
5.2 Independentcomponentanalysis(ICA) . . . . . . . . . . . . . . . . . . . 54
5.3 Expermentalresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3.1 CCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3.2 ICA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
6 ConclusionandFutureWork 69
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
6.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Bibliography 73
viii
List of Figures
Figure 2.1 EEG signals from an adult with twenty-one electrodes at various
sitesonthebrainscalp. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Figure2.2 Brainlobes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Figure3.1 64-Chstandardelectrodelayout. . . . . . . . . . . . . . . . . . . . 20
Figure3.2 Videorecordingfromtheexperiment. . . . . . . . . . . . . . . . . 22
Figure3.3 Thelocationofthreewebcameras. . . . . . . . . . . . . . . . . . . 23
Figure3.4 Fourstagesoftheexperiment. . . . . . . . . . . . . . . . . . . . . 23
Figure4.1 Theoverviewofmovement-triggeredartifactanalysis. . . . . . . . . 27
Figure4.2 SegmentationprocessandrelationbetweenvideoandEEG. . . . . . 28
Figure4.3 Threetypesofsegmentsinthetimedomain. . . . . . . . . . . . . . 31
Figure4.4 Powerspectraldensity(PSD)ofonesubjectinaspecificmovement. 35
Figure4.5 EEGsignaldecompositionbasedonDWT. . . . . . . . . . . . . . . 38
Figure4.6 Agraphicaluserinterface(GUI)fordataanalysis. . . . . . . . . . . 41
Figure4.7 AnexampleofPSDingraphicaluserinterface(GUI). . . . . . . . . 42
Figure 4.8 Comparison of means (a) and variances (b) of relative Beta2 power
foreachchannel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Figure4.9 StatisticalanalysisofrelativeBeta2powerforallchannels. . . . . . 44
Figure4.10 Statisticalanalysisofspectraledgefrequency(SEF)forallchannels
intestperiod. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Figure4.11 GrandmeanofrelativeBeta2powerinfourtypesofmovements. . . 47
ix
Figure 4.12 Comparison of means (a) and variances (b) of wavelet entropy for
threetypesofsegments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Figure4.13 Statisticalanalysisofrelativewaveletentropy(RWE)forallchannels. 48
Figure5.1 Generalframeworkofsignalcombination. . . . . . . . . . . . . . . 50
Figure5.2 Experimentalprocedureofmovement-triggeredartifactremoval. . . 51
Figure 5.3 Comparison of means (a) and variances (b) of relative Beta2 power
foreachchannelafterapplyingCCA. . . . . . . . . . . . . . . . . . . . . 58
Figure 5.4 Statistical analysis of relative Beta2 power for all channels after
applyingCCA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Figure5.5 Statisticalanalysisofspectraledgefrequency(SEF)forallchannels
inreconstructedperiodafterapplyingCCA. . . . . . . . . . . . . . . . . . 60
Figure 5.6 Comparison of means (a) and variances (b) of wavelet entropy for
eachchannelafterapplyingCCA. . . . . . . . . . . . . . . . . . . . . . . 61
Figure 5.7 Statistical analysis of relative wavelet entropy (RWE) for all chan-
nelsafterapplyingCCA. . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Figure 5.8 Comparison of means (a) and variances (b) of relative Beta2 power
foreachchannelafterapplyingICA. . . . . . . . . . . . . . . . . . . . . . 63
Figure 5.9 Statistical analysis of relative Beta2 power for all channels after
applyingICA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Figure5.10 Statisticalanalysisofspectraledgefrequency(SEF)forallchannels
incontextperiod(a)andtestperiod(b)afterapplyingICA. . . . . . . . . . 65
Figure 5.11 Comparison of means (a) and variances (b) of wavelet entropy for
eachchannelafterapplyingICA. . . . . . . . . . . . . . . . . . . . . . . . 66
Figure 5.12 Statistical analysis of relative wavelet entropy (RWE) for all chan-
nelsafterapplyingICA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
x