Bayesian Classification of Single-Trial Event-Related Potentials in EEG #

We present a systematic and straightforward approach to the problem of single-trial classification of event-related potentials (ERP) in EEG. Instead of using a generic classifier o#-the-shelf, like a neural network or support vector machine, our classifier

BayesianClassi cationofSingle-TrialEvent-RelatedPotentialsinEEG

JensKohlmorgenBenjaminBlankertz

FraunhoferFIRST.IDA

Kekul´estr.7,D-12489Berlin,Germany

E-mail:{jek,blanker}@ rst.fraunhofer.de

Abstract

Wepresentasystematicandstraightforwardapproachtotheprob-lemofsingle-trialclassi cationofevent-relatedpotentials(ERP)inEEG。Insteadofusingagenericclassi ero -the-shelf,likeaneuralnetworkorsupportvectormachine,ourclassi erdesignisguidedbypriorknowledgeabouttheproblemandstatisticalpropertiesfoundinthedata。Inpar-ticular,weexploitthewell-knownfactthatevent-relateddriftsinEEGpotentials,albeithardtodetectinasingletrial,canwellbeobservedifaveragedoverasu cientlylargenumberoftrials。WeproposetousetheaveragesignalanditsvarianceasagenerativemodelforeacheventclassanduseBayes’decisionrulefortheclassi cationofnew,unlabeleddata。ThemethodissuccessfullyappliedtoadatasetfromtheNIPS*2001Brain-ComputerInterfacepost-workshopcompetition。Ourresultturnsouttobecompetitivewiththebestresultofthecompetition。

1Introduction

TheanalysisofEEG(electro-encephalogram)isoneofthemostchallengingproblemsinsignalprocessingandmachinelearningresearch.Aparticularlydi culttaskistheanalysisofevent-relatedpotentials(ERP)fromindividualevents(‘single-trial’),whichrecentlygainedincreasingattentionforbuildingbrain-computerinterfaces.Theproblemisinthehighinter-trialvariabilityoftheEEGsignal,wheretheinterestingquantity,e.g.aslowshiftofthecorti-calpotential,islargelyhiddeninthe‘background’activityandonlybecomesevidentbyaveragingoveralargenumberoftrials.

Birbaumeretal.investigateslowcorticalpotentials(SCP)andhowtheycanbeself-regulatedinafeedbackscenario.Intheirthoughttranslationdevice

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