ContentslistsavailableatScienceDirect
Journal
of
Neuroscience
Methods
j ou rn a l h om epa g e : w w w . e l s e v i e r . c o m / l o c a t e / j n e u m e t h
Real-time
EEG
artifact
correction
during
fMRI
using
ICA
Ahmad
Mayeli
a,b,
Vadim
Zotev
a,
Hazem
Refai
b,
Jerzy
Bodurka
a,c,d,∗ aLaureateInstituteforBrainResearch,Tulsa,OK,USAbDepartmentofElectricalandComputerEngineering,UniversityofOklahoma,Tulsa,OK,USA cStephensonSchoolofBiomedicalEngineering,UniversityofOklahoma,Norman,OK,USA dBiomedicalEngineeringCenter,UniversityofOklahoma,Norman,OK,USA
h
i
g
h
l
i
g
h
t
s
•Areal-timemethodbasedonICA(rtICA)isproposedtoremoveartifactsfromEEGdataacquiredsimultaneouslywithfMRI.
•ThertICAeffectivelyreducesocular,motion,BCG,muscleandresidualMRartifactsandretrievesEEGsignals.
•ThertICAmethodfollowingthertAASoutperformsthertAASforremovingartifactsinrealtime.
•ThertICArevealedreliableartifactsuppressionresultsforfurtherapplicationsofreal-timemultimodalEEG-fMRI.
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Articlehistory:Received18July2016
Receivedinrevisedform8September2016 Accepted29September2016
Availableonline30September2016
Keywords:
Real-timeartifactcorrection fMRI
EEG EEG-fMRI Real-timeICA
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Background:SimultaneousacquisitionofEEGandfMRIdataresultsinEEGsignalcontaminationby imag-ing(MR)andballistocardiogram(BCG)artifacts.ArtifactcorrectionofEEGdataforreal-timeapplications, suchasneurofeedbackstudies,isthesubjectofongoingresearch.Todate,averageartifactsubtraction (AAS)isthemostwidespreadreal-timemethodusedtopartiallyremoveBCGandimagingartifacts with-outrequiringextrahardwareequipment;noalternativesoftware-onlyrealtimemethodsforremoving EEGartifactsareavailable.
Newmethods:Weintroduceanovel,improvedapproachforreal-timeEEGartifactcorrectionduring fMRI(rtICA).ThertICAisbasedonrealtimeindependentcomponentanalysis(ICA)anditisemployed followingtheAASmethod.ThertICAwasimplementedandvalidatedduringEEGandfMRIexperiments onhealthysubjects.
Results:OurresultsdemonstratethatthertICAemployedafterthertAAScanobtain98.4%successin detec-tionofeyeblinks,4.4timeslargerINPSreductionscomparedtoRecView-correcteddata,andeffectively reducemotionartifacts,aswellasimagingandmuscleartifacts,inrealtimeonsixhealthysubjects.
Comparisonwithexistingmethods:Wecomparedourreal-timeartifactreductionresultswiththertAAS andvariousofflinemethodsusingmultipleevaluationmetrics,includingpoweranalysis.Importantly, thertICAdoesnotaffectbrainneuronalsignalsasreflectedinEEGbandsofinterest,includingthealpha band.
Conclusions:Anovelreal-timeICAmethodwasproposedforimprovingtheEEGqualitysignalrecorded duringfMRIacquisition.Theresultsshowsubstantialreductionofdifferenttypesofartifactsusing real-timeICAmethod.
©2016TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCC BY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Electroencephalography (EEG) and functional Magnetic Res-onance Imaging (fMRI) are widely used, noninvasive, and safe techniquesfordetectingandcharacterizingchangesinbrainstates andtheirrelationtobrainactivity(RitterandVillringer,2006).The
∗Correspondingauthorat:LaureateInstituteforBrainResearch,Tulsa,OK,USA.
E-mailaddress:[email protected](J.Bodurka).
techniquescomplementeachotherwellbecauseofhigh tempo-ralresolutionofEEGdataandhighspatialresolutionoffMRIdata
(Niazyetal.,2005).Furthermore,becauseEEGisadirectmeasureof
brainactivityandfMRIisanindirectmeasure,simultaneous EEG-fMRImeasurementscanaidincrossvalidation.However,recording EEGinsidetheMRIscannerandduringfMRI acquisitionsuffers fromseveralsafetyandtechnicalchallenges(Kruggeletal.,2000).A majorproblemisthepresenceofartifactsinEEGdata,suchasMRor imagingartifactsandalsoballistocardiogram(BCG)artifacts.BCG andimagingartifactsappearintheEEGsignalasaresultofthe
sig-http://dx.doi.org/10.1016/j.jneumeth.2016.09.012
0165-0270/©2016TheAuthor(s).PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
nalbeingrecordedinsideMRIscannerandduringfMRIacquisition respectively(Niazyetal.,2005).Othertypesofartifacts,suchas muscleandocularartifactscanbepresentinEEGdataregardless iftheEEGisrecordedinsideoroutsidetheMRIscanner(Mantini
etal.,2007;McMenaminetal.,2010).
Theaverageartifactsubtractionmethod(AAS)(Allenetal.,1998, 2000)iscommonlyusedtoremoveBCGandimagingartifacts.To date,itisthemostwidespreadreal-timemethodusedtopartially removesuchartifacts.TheAASmethodisbasedontherepetitive patternofimagingandBCGartifacts,anditgeneratesanartifact templatetosubtractitfromtheEEGsignal.EventhoughtheAAScan effectivelyreduceBCGandimagingartifacts,someresidualartifacts remainwhenthisalgorithmisappliedtorawEEGdatainbothreal timeandoffline(Niazyetal.,2005).Highquality,modernMRI scan-nergradientcontrollerstogetherwithsynchronizationofMRIand EEGsystemclocksenablegenerationofaccurateandreproducible templatesofgradientartifacts,andallowforAAStemplate subtrac-tionthathasprovenextremelysuccessful(Laufs,2012).However, temporalvariabilityofBCGartifactmakesremovalofBCGartifact usingtheAASlessefficient.WhiletheAAShasprovensuccessful forreducingBCGand,especially,imagingartifacts,themethoddoes notremoveocular,motion,andmuscleartifacts.Instead,ICAhas beenwidelyusedinofflineanalysisasanalternativefor attenu-atingresidualimagingandBCGartifacts,andotherartifacts(e.g.,
Mantinietal.,2007;McMenaminetal.,2010;Srivastavaetal.,2005;
Wongetal.,2016;Zotevetal.,2016).AvarietyofICA-based
meth-ods(e.g.,FastICA,extendedInfomax,RobustICA,JADE,andSOBI) havebeenutilizedforthispurpose.Morerecently,Hsuetal.(2016)
demonstratedthatonlinerecursiveICAalgorithmsarefastenough forreal-timeEEGsourceseparation.However,theydidnotsuggest anyautomaticalgorithmforidentifyingartifactsamongsourcesin theirstudy.
ProblemsassociatedwithEEGartifactshaveledtothe devel-opmentofa number ofalternativemethods forremoving fMRI environmentandregularphysiologicalartifacts.Niazyetal.(2005)
suggesteda novelmethod,namelyoptimalbasis set(OBS), for generatingBCG artifact templates.They used principal compo-nentanalysis(PCA)forcapturingtemporalvariationsinartifacts andregressing BCGartifactsfromEEGdata.Theresultis supe-riorperformanceovertheAASforremovingBCGartifacts,with fewerresidualartifactsremaining.Thismethodhasrecentlybeen adoptedforreal-timeartifactcorrection(Wuetal.,2016).Likethe rtAAS,thismethodcanonlyremoveBCGandimagingartifactsand theapplicationofthismethodisobscuredwhentheaccuracyof R-peakdetectionislowduetoECGdatadistortion.Furthermore, sincemuscleandespeciallyocularandmotionartifactsoftenhave greateramplitudescomparedtoneuralactivityandhigheror sim-ilaramplitudetoBCGartifacts,theinteractionofmotion,muscle andocularartifactsonBCGartifacttemplateneedstobe inves-tigatedfurther (Wu et al., 2016).Kim et al. (2004)proposed a combinationofwavelet-basedde-noisingwithadaptivefilteringas post-processingtoincreasetheAASperformance.Likewise, adap-tivenoisecancellationwassuggestedasapreprocessingstepfor
theOBS(Niazy etal.,2005).PCAhasalsobeenusedfor
remov-ingBCGandimagingartifactsinstudiesreportedinNegishietal.
(2004)andBénaretal.(2003).Wavelettransform,followedbyICA,
hasproventobeausefulmethodforremovingartifacts(Akhtar
etal.,2012;ZhouandGotman,2004).
SeveralresearchersutilizedreferencesignalsforremovingBCG artifacts(Bonmassaretal.,2002;DunseathandAlden,2010;Luo
etal., 2014;Masterton etal., 2007;vander Meeretal., 2016).
Bonmassaretal.(2002)utilizedapiezoelectricmotionsensorto
estimatemotionartifactnoise.Correlationbetweenmotionsensor andEEGsignalwasusedtodesigntheKalmanfilterforremoving BCGartifacts.Mastertonetal.(2007)introducedawire-loop-based methodforcorrectionofmotionandBCGartifacts.Dunseathand
Alden (2010) suggested using reference electrodes attachedto
a conductive referencelayer for recordingartifacts and further removingnoisefromEEGdata.Althoughthesemethodsappear beneficial for reducing artifacts,they are not yet widely used. Unfortunately,thesemethodsrequirehardwaremodificationand additionalequipment,whichmakesthemcomplicatedandmore expensivetoimplement(Jorgeetal.,2015).Furthermore,someof thesemethodsrequirecomplicatedandtimeconsuming calcula-tions,whichmakethemlesssuitableforreal-timeapplications.
Real-timeimagingandBCGartifactcorrectiontechniqueswere usedinseveralsimultaneousEEGandfMRIstudies(Beckeretal.,
2011;Cavazzaetal.,2014;Zichetal.,2015;Zotevetal.,2014).
TheAASimplementedinrealtimeintheRecViewsoftware(Brain ProductsGmbH,Gilching,Germany)wasusedinallofthesestudies forreducingBCGandimagingartifacts.Developingnewreal-time algorithmsforremovingEEGartifactswouldmakereal-time anal-ysisofmultimodalEEG-fMRIsignalsmorefeasibleandthusopen manynewresearchopportunitiestostudyhumanbrainfunction.
Inthiswork,anovelreal-timeEEGartifactcorrectionapproach duringfMRI(rtICA)isdeveloped.ThertICAisareal-timeICA-based algorithmforreducingBCGandimagingartifacts,inadditionto motion,ocular,andmuscleEEGartifacts,andtoimproveEEGdata qualityacquiredduringfMRI.ImagingandBCGartifactsarefirst reducedusingtheBrainVisionRecViewsoftwareinrealtimeprior toapplyingthertICA(rtAAS+rtICA).Thefollowingsectionprovides amoredetaileddescriptionofourproposedrtICAmethod.Sincethe EEGactivityischangingduringtimeandindifferentconditions, insteadofcomparingEEGdatarecordedduringfMRIacquisition andoutsideMRIscanner,wepreferredtocomparetheperformance ofthertAAS+rtICAforremovingartifactswithRecView-corrected dataandEEGdataafterapplyingdifferentofflineartifact correc-tions.Finally,wediscussresultsandimprovementoftheEEGdata quality.Apreliminaryreportofportionsofthisworkwaspresented
inMayelietal.(2015).
2. Methods 2.1. Dataacquisition
Thestudy wasconductedat theLaureate Institutefor Brain ResearchwithresearchprotocolapprovedbytheWestern Insti-tutional Review Board (IRB). All participants provided written informedconsentandreceivedfinancialcompensationfor partici-pation.
ThertAAS+rtICAartifactremovalmethodhasbeentestedon sixhealthysubjects(meanage:36±14years,threefemales).Four restingEEG-fMRIrunswereconducted;eachrunlasted8min40s. Theparticipantswereinstructedtorelaxandrestwitheyesclosed fortworuns,andthenkeeptheireyesopenandfixedonacross foranadditionaltworuns.Sequencerunswitheyes-closedand eyes-openwasbalanced toeliminate fatiguefactor(Yuanetal.,
2013).
MR images were acquired via a General Electric Discovery MR750whole-body3TMRIscannerwithastandard 8-channel, receive-only headcoil array.For fMRIacquisition, a single-shot gradient-recalledEPIsequencewithSensitivityEncoding(SENSE) wasemployed.TheEPIsequencewascustommodifiedtoensure thattherepetitiontimeTRwasexactly2000ms(with1s accu-racy)andfurtherenabling accuratecorrectionofMRartifactsin EEGdata, recorded simultaneouslywithfMRI. EPI imaging had thefollowingparameters:FOV=240mm,slicethickness=2.9mm, slice gap=0.5mm, 34 axial slices per volume, 64×64 acqui-sition matrix, echo time TE=30ms, SENSE acceleration factor R=2, flip angle=90◦, sampling bandwidth=250kHz. The fMRI runtime was 8min40s. For allowingthefMRI signalto reach
Fig.1. Proposeddataprocessingproceduresuitableforreal-timeimplementation.SOBIwasappliedonthewhole10,000pointslidingwindow,butthecorrectionwasonly performedonthefinal1000pts.
steady state, three EPIvolumes (6s) were added at the begin-ningoftherunandwereexcludedfromdataanalysis.ThefMRI voxel size was 3.75×3.75×2.9mm3. For acquiring anatomical
image,aT1-weightedmagnetization-preparedrapidgradient-echo (MPRAGE)sequencewithSENSEwasused.TheMPRAGEsequence had the following parameters: FOV=240mm, axial slices per slab=128,slicethickness=1.2mm,imagematrixsize=256×256, TR/TE=5.0/1.9ms, SENSE factor R=2, flip angle=10◦, delay time TD=1400ms, inversion time TI=725ms, sampling band-width=31.2kHz,scantime=4min58s.
EEGsignalswererecordedsimultaneouslywithfMRIviaa 32-channelMR-compatibleEEGsystemfromBrainProductsGmbH. TheEEG capincluded32 electrodes, arranged accordingtothe international10–20system.Oneelectrodewasplacedonthe sub-ject’sbackforrecordingtheECGsignal.ABrainProducts’SyncBox devicewasusedtosynchronizetheEEGsystemclockwiththe 10MHz MRIscannerclock.EEGacquisitiontemporal resolution was0.2ms(i.e.,16-bit5kS/ssampling),andmeasurement resolu-tionwas0.1V.EEGsignalswerehardwarefilteredthroughoutthe acquisitioninafrequencybandbetween0.016and250Hz.Brain Products’RecViewsoftwarewasusedtomonitorEEGdata acqui-sitioninrealtimeandtoreduceimagingandBCGartifactsbefore streamingtothertICAmodule.
2.2. Secondorderblindidentification
Second order blind identification (SOBI) (Belouchrani et al., 1997) takes advantage of temporal correlations in the source activities. It calculates second order statistics–covariance matrices–which are later diagonalized. The SOBI differs from otherblindseparationalgorithmsbyitsrobustness,thelow num-beroftunableparametersrequiringadjustment,andconvergence speed,whichisthemostimportantreasonforchoosingthisICA algorithmforthisstudy.Table1presentstheaveragecomputation timerequiredforICAdecompositionof40s22-channeldatausing differentICA algorithms,computed by averagingthe execution
Table1
Averagecomputationtime(s)for40s(10,000datapoints)22-channeldatausing differentICAalgorithms.
Time(s)
Infomax 13.56±1.23
JADE 2.96±1.28
FastICA 6.74±4.54
SOBI 0.71±0.10
time for 3 subjects duringan 8min and 40sresting staterun. The ICA computations were performed on MATLAB 2012. The ICAMATLABcodesavailableinEEGLABtoolboxandtheirdefault values forICAconvergencewereutilized.Amongthepresented fouralgorithms,SOBIhasthelowestaveragedtimeandstandard deviation. Based on Klemm et al. (2009) study comparing ICA algorithms, the SOBI has been shown to provide some of the best-qualityresultsinseparatingEEGdataandartifacts.
2.3. ProposedICAdataprocessingsuitableforreal-time implementation
Theproposeddataprocessingproceduresuitableforreal-time implementationisshowninFig.1(Mayelietal.,2015).Toobtain reliableandstableresultsfromICAdecomposition,datasubmitted tothealgorithmshouldbeatleastamultiplekofn2,wherenis
thenumberofchannelsandkmayneedtobe20orlarger(Onton etal.,2006).Forinstance,given31-channeldataandk=20,data sampleswillbeatleast19,220.Atasamplingrateof250S/s,data windowdurationisapproximately80s.Inthisstudy,22channels wereselectedtoincreasealgorithmspeed,asopposedto perform-ingICAonallchannels.Whenn=22andk=20,thenumberofdata pointssubmittedtoICAshouldbemorethan9680–roundedup to10,000–datapoints.Theselectedchannels,shownin Fig.2, includealltheavailablefrontalEEGchannels,becauseEEGactivity overthefrontalandprefrontalbrainregionsisrelevanttoemotion regulationandisusedforneurofeedbackstudies(Cavazzaetal.,
Fig.2. Selected22channelsforICAdecomposition.
Fig.3.rtAAS+rtICAflowchart.
2014;Zotevetal.,2014,2016).Occipitalelectrodes(i.e.O1,O2and
Oz),whichareimportantforinvestigatinghumanvisualevoked potential,wereselectedforICAdecompositionaswell(Beckeretal.,
2008,2011).Theotherchannelswereselectedbasedontwo
crite-ria.First,sincetheBCGartifactsmostlyexhibitbipolarproperties, weselectedsymmetricchannelsforICAdecomposition.Second,the channelswhicharecontaminatedwithartifactsandhavecommon artifactswithoccipitalorfrontalelectrodes(e.g.channelsT1and T2areaffectedbymuscleartifacts)wereselected.Theseselected channelsand numberofchannelscanbechangedbasedonthe applicationofreal-timeEEG-fMRIstudyandnumberofEEGsystem electrodes.
Fig. 3 offers a detailed flow diagram description of the rtAAS+rtICAprocess.TheSOBIalgorithmwasimplementedinthe followingway.TheRecViewsoftwarewasutilizedtoreduce
imag-ingandBCGartifactsfromthe32-channelEEGdatainrealtime,and thentodown-sampledatato250S/sfrom5000S/s.RecViewoutput datawereexportedin8msblocks(i.e.,twodatapointsperblock forallchannels).Thenumberofdatapointsisfirstinitializedto zero.Duringpreprocessing,newlyarrivingdatapointsarecounted. Sincethisschemerequires23channels(i.e.,22EEGchannelsplus 1 ECG electrode) for ICAdecomposition, data pointsrelated to thesechannelsareselected.ICAdecompositioncommenceswhen totalnumberofdatapointsreaches10,000.Fourfeatures(power spectrumdensity,topographicmap,kurtosis,energy)areextracted fromthelast1000datapointsofeachindependentcomponent(IC). Basedonextractedfeaturesanddefinedthresholds,amaximumof 12ICsforvarioustypesofartifactsareconsidered(i.e.,amaximum ofthreecomponentsforocular,threeformotion,threefor mus-cleandresidualimagingartifact,andthreecomponentsforBCG artifact).ThenumberofICsidentifiedasartifactswaslimitedto preventlosinganybrainactivity.AftersubtractingartifactualICs, inverseICAreconstructsEEGdata,andthenumberofdatapointsis setto9000.Thisprocedureisrepeatedforeach1000datasamples received.
Anexampleapplicationofreal-timeEEGacquisitionisEEG neu-rofeedback.FrontalEEGasymmetryisapromisingtargetforEEG neurofeedbackaimedattrainingemotionregulation,asconfirmed byrecent simultaneousEEG-fMRI studies(Cavazzaet al.,2014;
Zotevetal.,2014,2016).Whenusedasareal-timetargetforEEG
neurofeedback,frontalEEGasymmetryinaspecificEEGbandis definedasZotevetal.(2014):
A= [P(F3)−P(F4)]
[P(F3)+P(F4)] (1) wherePistheEEGpowerforagivenchannelinaspecificEEGband suchashigh-betaoralphabands.FrontalEEGasymmetryinthe alphaEEGbandisdefinedwiththeoppositesign(Cavazzaetal.,
2014;Zotevetal.,2016).
2.4. AutomaticICclassification
VariousfeaturesareextractedfromICsandusedtoclassifyICsas brainactivityandartifacts.Thefirstisthetopographicmapofeach independentcomponent,whichindicatesscalpmapprojectionof selectedcomponents(i.e.,eachcomponentprimarilyaffectinga specificportionofthebrainandcanbedeterminedusinganICA mixturematrix).Thevaluesofthetopographicmapwere normal-izedtounityusinginverseICAmatrices.ThesecondfeatureisIC energy.Theenergyofadiscretetimesignalofxisdefinedby:
Ex
∞n=−∞|x[n]|
2 (2)
Kurtosiscanalsobeusedforseparatingartifactsanditisa mea-sureofdatadistribution(i.e.,peakednessorflatness)thatcanbe calculatedusingthefollowingequation:
kurt(x)=E
x4−3Ex22 (3)ThefinalfeatureispowerspectraldensityofICcalculatedvia theFastFourierTransform(FFT)toindicatedistributionofsignal powerinfrequencydomain.Fordetectingeye,motionandresidual BCGartifacts,thespectrum intherangebetween0.5and40Hz wasnormalizedtotherangeof0–1andfordetectingmuscleand residualMRartifact,thespectrumintherangebetween0.5and 60Hzwasnormalizedtotheunity.
TheEEGdatawererecorded fromthree healthyfemale sub-jects(meanage:26±6years)andthreemalesubjects(meanage: 29±9years)diagnosedwithcombatrelatedpost-traumaticstress disorder(PTSD)acrossaneurofeedbackexperiment(3640s)and wereusedtodefineareliablethresholdforeachfeatureandtoavoid removingbrainactivityinsteadofartifacts(Zotevetal.,2014).
Table2
ThesummaryoftheidentificationoftheICsbasedonenergy,kurtosis,topographicfeaturesandspectrumcharacteristics.
TypesofArtifacts Energy Kurtosis TopographicFeatures SpectralCharacteristics EyeArtifacts >108 >6 Prefrontalregion(Fp1andFp2)
withalargerthresholdvalueof 28%
Normalizedpowerbetween0.5 and3Hz>0.22
ResidualBCGArtifacts – – Bipolarwiththethreshold valueoflargerthan0.25 AND
Occipitalregionwithasmaller thresholdvalueof14%
Normalizedpowerbetween2 and7Hz>0.18
AND
Normalizedpowerbetween7 and12Hz<0.12
MuscleandResidualImagingArtifacts – – Unipolar Normalizedpowerbetween30 and60Hz>0.68
AND
Normalizedpowerbetween7 and12Hz<0.07
MotionArtifacts >109 >15 Bipolarwiththethreshold
valueof0.2
Normalizedpowerbetween0.5 and4.5Hz>0.26
Fig.4.Automatic(a)eyeartifact,(b)residualBCGartifact,(c)residualMRandMuscleartifactand(d)motionartifactdetectionandresultsofremovingthemfromEEGdata.
Table2summarizesreal-timeclassificationcriteriaforeachtype
ofartifactsbeingdetectedincludingnumericalthresholdvaluesfor kurtosis,energy,topographicfeaturesandspectralcharacteristics. Thedescribedcriteriaarepreliminaryandneedfurther optimiza-tion.Fig.4showsanoverviewofautomaticartifactICidentification.
2.4.1. Eyeartifactdetection
Eyemovementandblinkingproducesanelectricalpotentialat amagnitudelargerthanbrainactivity.Duetoeyeproximity,ocular artifactsmostlyaffectfrontalpoleelectrodes(i.e.channelsFp1and Fp2),althoughtheycanpropagateacrossmuchoftheheadand distortbrainsignals.Ocularartifactsarecharacterizedbystrong spatialprojectionintheprefrontalarea,eithershowshighloadings
atthemostanteriorsitesforeyeblinksormanifestsasananterior dipoleforsaccades,highenergyandlow-frequencypeaks(between 0.5and3Hz)inthefrequencydomain(McMenaminetal.,2010; Wongetal.,2016).Basedonsuchfeatures,ocularartifactscanbe easilydetectedamongdifferentICs.
2.4.2. ResidualBCGartifactsdetection
BCGartifactsareextremelyproblematicinEEGduetotheir over-lapwithfrequencyrangeofnormalneuralactivity.Althoughthe RecViewisoftenusedtopartiallyremoveBCGartifacts, remain-ingresidualartifactscanobscuretheEEGsignalbecause:(1)ECG signalrecordedviachestorbackelectrodesduringfMRIcontains artifactsthatcanmakereliableR-peakdetectiondifficult(Luoetal.,
2014); and (2) AAS algorithm assumes pulsewaveform with a highlyreproduciblepatternwithina giventimeinterval.In this way,subtractingaveragewaveformfromcontaminated EEGcan appropriatelyremoveBCGartifacts.Notably,thisassumptionhas notbeenproven(Kimetal.,2004).Toreducesuchartifacts,IC fea-tures(e.g.,highrelativepowerofthetabandactivitybetween2 and7Hz)relatedtoBCGartifactscanbeleveraged(Wongetal., 2016).Furthermore,topographicalmapscorrespondingto resid-ualBCGartifactsexhibitprimarilybipolarproperties(Zotevetal., 2012).Thebipolarpropertiescouldbeappearinrightsideofhead (i.e.channelsF8,FC6,C4,CP6,P8andT8)andleftsideofhead(i.e. channelsF8,FC6,C4,CP6,P8andT8)orfrontal(i.e.Fp1,Fp2,F7, F3,Fz,F4,andF8)andposterior(i.e.P7,P3,P4,P8,O1,Oz,andO2) sidesofthehead.BCGartifactscontainfairlylargepeaks,asshown inFig.4(b)andhavehighenergy.RemovingsuchICseffectively reducesBCGartifacts.
2.4.3. Muscleandresidualimagingartifactsdetection
AlthoughtheRecViewisusedtoreduceimagingandBCG arti-factsbeforeapplyingICA,someresidualartifactsremain.Residual imagingandmuscleartifactsareprimarilydistinguishedbasedon powerspectrawithbroadpeaksathigherfrequencies(i.e.,higher thanregularEEGsignal)(McMenaminetal.,2010).Becausesuch artifactscouldappearinthebetaband,onlyICswithhighfrequency activityat30Hzandhigherareconsideredmuscleandimaging arti-facts(SeeFig.4(c)).Afterreducingsuchartifacts,mostsharpspikes withhighamplitudesareremovedfromtheEEGsignal.
2.4.4. Motionartifactdetection
Head,body, or electrode movement artifacts are easily rec-ognized offline as a result of their large amplitude. Intervals containingtheseartifactscanbeexcludedwithoutdifficulty,given thatsuchactivitydoesnotoccurfrequentlyformostsubjects. Real-timeanalysisofsuchdataisdifficultbecauseoftherandomnature ofmotionartifacts.MotionartifactICsarecharacterizedbyhigh kurtosisvalues(oftheorderof10–1000),intenselow-frequency activity(0.5–4.5Hz)andbipolartopographiessimilartoBCG arti-factswithdifferentthresholdvalues(Wongetal.,2016;Zotevetal., 2012).Becauseofthelargemagnitudeoftherandom-motion arti-facts,theymayappearacrossmultipleICsintheICAdecomposition. Removingmovementartifactsmayremovesomebrainactivityas well.Topreventthis,thenumberofIC-detectedmovement arti-factsislimitedtoonlythosecomponentswithverylargeenergy andkurtosis,aswellasthosewithlowfrequencyactivity.Fig.4(d) showsICidentifiedasmotionartifacts,aswellastheoutcomesafter removingthem.
2.5. Implementation
TheproposedalgorithmwasimplementedinMATLAB (Math-WorksInc.,Natick, MA) for offlinetesting. TheSOBI algorithm, implementedinEEGLAB(DelormeandMakeig,2004)wasusedfor ICAdecomposition.
For real-timeimplementation, software codewaswritten in Pythonprogramming languageasanadd-onmodulefor simul-taneousrtfMRI-EEGneurofeedbacksystemasdescribedinZotev
etal.(2014).Real-timeICAdecompositionofRecView-corrected
EEGdatawasaddedtotheEEGclientsoftwareforremoving arti-factsfromsignalsbeforeperformingMathmodulesandintegrating EEGandfMRI.Thisprocessingmodulewasnamed“eegrtica”. Mod-uleoutputsweresavedtofilesinrealtimeforfurthercomparison withRecView-correcteddataandofflinemethods.
2.6. Offlineprocedureforremovingartifactsforevaluating rtAAS+rtICAalgorithm
TheBrainVision Analyzer2 software (Brain Products GmbH, Germany)wasusedforofflineanalysisoftheEEGdatarecorded simultaneouslywithfMRI.DifferentstudiesshowthatICA follow-ingeithertheAASortheOBSalgorithmscansuccessfullyremove residualartifacts(Debeneretal.,2008;Mantinietal.,2007;Zotev
etal.,2014,2016).Accordingly,forthisstudyweprimarilyusedthis
methodasareferencetoevaluatethertAAS+rtICAperformance andcompareitwithothers.TheprocedureforofflineEEGartifact removalusingtheAAS,filteringandICA(AAS+Filtering+ICA)is thefollowing.First,imagingartifactswereremovedusingtheAAS method(Allenetal.,2000).EEGsignalswerethendown-sampled to250S/s.Next,band-rejectionfilters(1Hzbandwidth)wereused forremovingfMRIsliceselectionfundamentalfrequency(17Hz) anditsharmonics,vibrationnoise(26Hz),andACpowerlinenoise (60Hz).Theseband-rejectionfiltersshouldbeselectedbasedonthe MRIscannerandsequenceproperties.Forremovingsignals unre-latedtobrainactivity,EEGdatawerebandpassfilteredfrom0.1to 80Hz(48dB/octave).FordetectingR-peaksmoreeasily,ECGdata werebandpassfilteredfrom0.1to12Hz.Subsequently,BCG arti-factswereremovedusingtheAAS(Allenetal.,1998).Atemplate ofBCGartifactsfrom21cardiacperiodsofprecedingdataforeach channelwasusedtocreateatemplateforremovingBCGartifact usingtheAAS.Notably,R-peakdetectionisrequiredforgenerating theBCGtemplate.Thiscanbeaccomplishedautomaticallyinthe BrainVisionAnalyzer2software,withsubsequentvisualinspection tocorrect incorrectlypositionedR-peakmarkers.Afterapplying theAASforreducingBCGartifacts,theInfomaxalgorithm(Belland
Sejnowski,1995)wasusedforICAdecomposition.Wechosethe
InfomaxasICAalgorithmsincethismethodiswidelyusedfor sep-aratingartifactsfromEEGdataandhasbeenproventobeamongthe mostreliableICAmethodsforremovingEEGartifacts(Nakamura
etal.,2006;Vanderperrenetal.,2010).However,wedidnotuse
thismethodforourrtICAimplementation,becausetheInfomaxis tooslowforourreal-timeapplication(Klemmetal.,2009).ICAwas performedon31channels,resultingin31ICs.Thetopographicmap, powerspectrumdensity,timecoursesignal,energyvalue,and kur-tosisvaluewereusedfordetectingandremovingartifactualICs. Afterwards,EEGsignalwasreconstructedusinginverseICA. Fur-thermore,wecomparedtheperformanceofthertAAS+rtICAwith twootherofflineartifactcorrectionmethods,consistingof remov-ing imaging artifacts using theAAS, reducing imaging artifacts usingAASandotherartifactsbymeansofourproposedautomatic ICA-basedmethod(thismethodwasimplementedoffline,butitcan beappliedreal-timeaswell).Finally,thertAAS+rtICAresultswere comparedwiththeRecView-correctedEEGdatawithimagingand BCGartifactsreducedinrealtimeusingtheAAS.
2.7. Evaluationmeasures
Variousevaluationmetricswereusedtodeterminetheability ofthertAAS+rtICAtoreduceEEGartifacts(and,moreimportantly, preservebrainactivity)andtocompareitsefficiencywiththatof offlinecorrection.
2.7.1. Thepercentagesofcorrectedeyeblinksdetected
Toevaluatethealgorithmperformancefordetectingeyeblinks, EEGdataweremanuallyinspectedtodetecteyeblinksandcompare resultstoautomaticallydetectedeyeblinksusingthertICA.
2.7.2. Powerspectraldensity
ToevaluatethertAAS+rtICAperformancegivennon-ideal con-ditions,wecomputedpowerspectraldensity(PSD)forall22EEG
Table3
Eyeblinkdetectionresultsfor6healthysubjectsduringeyesopenrestingstateruns.
NumberofEye BlinksDetected TrueNumberof EyeBlinks PercentageofEye BlinksDetected Subject1 190 193 98.45% Subject2 245 250 98.57% Subject3 82 84 97.62% Subject4 143 145 98.62% Subject5 71 75 94.67% Subject6 427 430 98.66%
channels beforeand after applying differentartifact correction methods.
2.7.3. INPSreduction
ToquantifychangesandcorrectedimprovementsofBCG arti-facts,thefollowingNormalizedPowerSpectrumratio(INPS)was computedas(Srivastavaetal.,2005;Tongetal.,2001):
INPS= N i=1 PSD beforeBCG reduction i N i=1 PSD after BCG reduction i (4)
whereNisthenumberofharmonicsofECGandPSDiisthepower
spectraldensityoftheithECGharmonic.
2.7.4. Powerreductioninmotiontraces
Motionartifactsaffectpowerspectrumsignificantly.To deter-minetheefficiencyofthertAAS+rtICAforreducingsuchartifacts, wecomparedthepowerbetween0.488and40.039Hzusing dif-ferentalgorithmintraceswhichwerecontaminatedwithmotion artifacts.
3. Results
Fig.5showsa4slongsegmentofRecView-correctedEEGdata (i.e.,22channels)acquiredsimultaneouslywithfMRI,aswellasthe sametraceofrawEEGrecordingsafterremovingimagingartifacts, rtAAS+rtICAcorrectedsignal,AASforimagingartifactsand auto-maticICAcorrectedEEGdata,and,finally,thecleanEEGdatausing theproposedofflineprocedure.
Fig.6shows22ICsretrievedfromRecView-correcteddatawith SOBIusingequivalenttracespresentedinFig.5.Inthisexample, IC2,5,and6wereidentifiedaseyeartifact;IC1,3,and7were classifiedasresidualBCGartifact;andIC22weredeterminedasa vibrationnoiseat26Hz.
Table3showsthenumberofeyeblinksdetectedusing
auto-maticrtICAmethod,aswellasthenumberofactualblinks.Also detailedisthepercentageofcorrectlydetectedeyeblinksforthe secondandfourthruns(i.e.eyesopenruns)forthesixhealthy sub-jects.TheresultsdemonstratethatrtICAeffectivelyremoveseye blinkartifacts.Accuratelydetectedeyeblinksforallsubjectswere morethan94%.
Fig.7comparestheperformanceoftheBrainVisionrtAAS,the rtAAS+rtICAandtheofflinemethodsintermsofINPSratio.The INPSratiosforartifactcorrectionmethodswerecomputedwith respecttotheEEGdataobtainedafterremovingimagingartifacts viaofflineAAS.
Fig.8demonstratesthat,formostsubjects,thertAAScouldnot reduceeffects of motionartifactonpowervalue.However,the rtAAS+rtICAandtheofflineautomaticICAcanreducepowerduring motiontracesandcanmakeitapproximatethepowervaluebased ontheofflineAAS+Filtering+ICAartifactcorrection.ThertICAcan significantlyreduceeffectofmotionartifactsinallsubjects.
Fig.9 providesanexample ofthepower spectraldensity of EEGdataforasubjectduringeyesclosedrunusingvariousartifact removalalgorithmsandshowsthatthertAAS+rtICAcaneffectively
reduceartifactsindifferentEEGfrequencybandswithoutaffecting theneuronalalphaandbetaactivities.
Finally,Fig.10illustratesthepowerinseveralEEGbandsbefore andafterartifactcorrection.FromFig.10(a),it canbeobserved that, for allsubjects,the RecView-correctedEEGdata have the highestpowervalueinthedeltafrequencybandwhencompared to thertAAS+rtICA and two other artifact correctionmethods. ThertAAS+rtICAandofflineautomaticICAmethodsreducethis power and make it approximate the EEG data after using the AAS+Filtering+ICAartifactcorrection.Notably,thedeltapower reflected mostly ocularand motionartifacts.Fig.10(b) demon-strates theta power of corrected EEG data using a number of algorithms.BCGartifactsaffectthethetabandpower.Again,the rtAAS+rtICAcanreduceartifactsmoreeffectively thanonlythe rtAASandtheofflineautomaticICA.Muscleandresidualimaging artifactsprimarilyaffectamplitudeofpowerspectruminthebeta frequencyband.ItiscriticalthatthertICAalgorithmnotmistakenly removebrainactivity,asignificantportionofwhichisreflected in thealphaband power,shown in Fig.10(c).The figure illus-tratesthateventhoughthealphapowervaluewasreducedusing thertAAS+rtICAandofflineautomaticICA,thevaluedoesnotdip belowalphapowerusingtheofflineAAS+Filtering+ICAartifact correction.Thisamountservesasourreferencefor nearly-artifact-freeEEGdata.Furthermore,Fig.10(c)and (d)demonstratethat withoutthertAAS,thertICAperformspoorlywhenusedtoreduce effectsofartifactsonthealphaandbetafrequencybands.From
Fig.10(d),wecanobservethatthertAAS+rtICAcouldmore effec-tivelyreducesuchartifactswhencomparedtotheperformanceof thertAASandrtICA.
4. Discussion
Fewmethodshavebeendevelopedforreal-timeremovalofBCG andimagingartifactsfromEEGdatarecordedduringfMRI.Thishas limitedtheapplicationofreal-timeEEG-fMRIsystems.Wepropose anovelalgorithmbasedonICAforattenuatingalltypesofartifact inEEGdataacquiredduringfMRIscans.Theproposedreal-time ICAmethodcanfurtherbeutilizedforanyapplicationrequiring real-timeICAdecomposition.
Althoughthe rtAASis supposed toreduce BCGartifacts,we canseeinFig.7thattheaverageINPSvalueislessthanzerofor twosubjectswhenusingthertAAS.AveragingtheINPSamongsix subjectsshows4.4timeslargerINPSreduction wasachievedby usingrtAAS+rtICAcomparedtortAAS.Forsubject1,theenormous amountofmotionartifactspreventstheRecViewfromperforming well.Furthermore,theECGsignalfromsubjects1and4areseverely distorted, which makesdetecting R-peak and creatingaccurate BCGartifacttemplateinrealtimeimpossible.Exceptforsubject 4,INPSreductionbythertAAS+rtICAandofflineautomaticICAis comparablewiththatoftheAAS+Filtering+ICAmethod.Thepoor performanceofthertAAS+rtICAintermsofINPSforsubject4could betheresultofthelowperformanceofthertAAS.
Eye blink, motion and muscle artifacts are problematic and obscuretheEEGsignalqualitysignificantly,butsofar,thereisno robustreal-timemethodforremovingsuchartifacts.Theresults ofremovingsuchartifactsinTable3,Figs.8and10(d)showthat thertICAcansubstantiallyreduceeffectsoftheseartifactsonEEG signal.
Basedonevaluationmetrics,thertAAS+rtICAhassuperior per-formancewhencomparedtothertAASandthertICA.Onereason forthisphenomenonisthateithertheICAorAASmightnot effec-tivelyreduceBCGandimagingartifactsforsomesubjects.Thus, bothmethods complementeachotherwhenremovingimaging, BCG,andotherartifacts.
Fig.5.Representative4-sexampletracesofEEGdatarecordedduringfMRIacquisition(subject4duringeyesopenrun)afterapplyingdifferentofflineandreal-timeartifact correctionmethods.
Fig.7.INPSReductionforallchannelsusingdifferentartifactcorrectionmethodsversustheEEGdataafterremovingimagingartifacts.
Fig.8.CompressionofpowerduringmotiontracesusingAAS+rtICA,RecViewandofflineartifactcorrectionmethodswithpowerwithoutanycorrection(EEGdataafter applyingAASforimagingartifactcorrection).
Fig.10.TheimpactofdifferentartifactcorrectiononEEGpowerin(a)delta(b)theta(c)alphaand(d)betafrequencybands.Barheightsrepresentaveragesacrosschannels, anderrorbarsrepresentthestandarderroracrossICAwindows.Resultsareshownforall6subjectsusingaveragedatafromall22channelsandall4restingstateruns.
It islikely that ourproposedreal-time technique might not perform as efficiently as offline artifact detection techniques. Admittedly,severalfactorscanlessenthemethodefficiencyand canbefurtheroptimized.Themostimportantfactoristhe accu-racyofthereal-timeICclassificationthatseparatesvariousartifacts fromtheICsdescribingneuronalactivity.Theclassification crite-riaandnumericalthresholdsettingsusedinthisworkwillneedto beoptimizedandrefinedfurther.Forinstance,recordingtestdata beforestartingtheactualexperimentandusingthetestdataasan inputforamachinelearningalgorithmforclassifyingbrainactivity andartifactICsmayimprovetheefficiencyofthemethod.Using mutualinformationappearshelpfulinthis regard(Abbasietal.,
2015;Liuetal.,2012).Anotherimportantfactoristhealgorithm
speed.WhenthealgorithmisimplementedinMATLAB,lessthan 1sisrequiredforremovingartifactsfrom4sofEEGdata;between 2sand4sisrequiredforthiswhenrunningourstand-aloneand proof-of-conceptsoftwareimplementationwritteninPython. Per-formingtheICAdecompositionusingtheSOBIrequiresthegreatest amountoftime.Thus,implementingthisparticularcode(i.e.,ICA decompositionusingtheSOBI)inCython(Behneletal.,2011)or assistingCmightaidinspeedingupthealgorithm.Byincreasing themethodspeed, wecanapplyICAon31 channelsinsteadof 22channels.Thiscouldimprovetheperformanceofthemethod forartifactcorrection.TherecentstudydemonstratesthatOnline RecursiveICA(ORICA)canbesuccessfullyimplementedinrealtime forsourceseparation,butmoreinvestigationsareneededto eval-uatetheORICAefficiencyfordistinguishingICsrelatedtoartifacts frombrainactivities(Hsuetal.,2016).Eventhoughstillthereare opportunitiestospeedupthealgorithm,themethodcanbeusedfor real-timeneurofeedbackapplicationandreal-timemonitoringof brainactivitysincethertICAupdatesEEGresultsinthecomparable timewithfMRI(whichisequaltoTR).
5. Conclusion
RecordingEEGduringfMRIacquisitionleadstoEEGdata con-taminationwithlargefMRIenvironmentartifacts.MostEEG-fMRI studieshave reducedartifactsoffline;howeveronlyafew have involvedreal-timecorrection.WehaveintroducedICA-based
real-time EEG artifact correction during fMRI. This novel approach wassuccessfullyimplementedandimprovedreal-timeEEG arti-factsdetectionandremovalduringfMRIonall6healthysubjects. Theproposedalgorithmcanbeeffectivelyimplementedfor vari-ousapplicationsthatrequireareal-timeEEGsignalwithartifacts suppressed(e.g.,neurofeedbackand online monitoringofbrain activity). Our work representsan important first step towards awider useofICAmethodsinreal-timeEEG-fMRI applications, suchasdeterminingthe(functional)neuralcorrelatesoftheEEG rhythmsusedinrtEEGneurofeedbackandrtfMRIdata.
Acknowledgment
ThisworkwassupportedbytheU.S.DepartmentofDefense grantW81XWH-12-1-0607.
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