Download pdf - EEG Regression Model for BCI Cursor ControlEEG headset was used to measure brainwave activity (Figure 3) while BCI2000 program recorded data such as the EEG activity and cursor position

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Page 1: EEG Regression Model for BCI Cursor ControlEEG headset was used to measure brainwave activity (Figure 3) while BCI2000 program recorded data such as the EEG activity and cursor position

Background:Brain-ComputerInterface(BCI)systemshavebecomeasourceofgreatinterestintherecentyears.Establishingalinkwiththebrainwillleadtomanypossibilitiesinthehealthcare,robotics,orentertainmentfields.Electroencephalography(EEG)isonepopularnoninvasivetechniquetoestablishaBCI.Inthismethod,electrodesareplacedalongthescalptorecordtheelectricpotentialscreatedfromtheneuronsfiredinthebrain.Throughaparadigmknownas“imaginedbodykinematics,”asubjectcancontrolacomputercursorwithoutmakinganyovertmovements.ThismethodofusingimaginedbodykinematicsfromEEGhasshowntodecreasetrainingtimefromdaystominutescomparedtoothermethods.Thisplatformcouldleadtoimprovingneurorehabilitationprogramsaswellasmoreadvancedprostheticdevicesthatcanbecontrolledbythebrain.Itcanalsobebeneficialtoparalyzedpatientsorthosewithotherneurodegenerativediseasesthatinhibitsmusclemovementsincenoovertmovementsarerequiredtocontrolthecursor.

Objective:ThegoalofthisprojectwastoimprovethepredictionaccuracyforapreviouslydevelopedBCImodelthatusedlinearregressiontopredictcursorvelocityfromasubject’sthoughtsbytestingnewmethodsandnonlinearmodels.

Figure1:RawEEGandEmotiv EPOCheadset

Thetrainingphaseconsistsof5horizontaland5verticaltrialseachlasting1minute.Thesubjectwasinstructedtousetheparadigmofimaginedbodykinematicstotrackthemotionofanautomatedcursorusingtheirdominanthandasiftheywereusingacomputermousewhilemakingnoovertmovements(Figure2).

Figure2:Outlineoftrainingtaskusingonedimensionalmovement

Datafrom33subjectsweretestedusingdifferentcombinationsofthemostimportantchannelsfoundforvelocityprediction(Table1).ItwasdeterminedthatchannelcombinationF7,FC5,T8,FC6,F4,andF8providedthebestaccuracyforpredictinghorizontalvelocity,whileallthechannelswerebestforverticalvelocity.UsingonlyF7andF8achievedacceptableaccuracygivingthepotentialforamorecovenantrealworldapplicationwithasmallerheadset.Futureworkwillincludetestingthesefeaturesinnewmachinelearningmodelstoimprovethepredictionscoresfromthelinearregressionmodel(Figure5).

Table1:Predictionaccuracyusingdifferentchannelcombinations

Figure5:Actualandpredictedvelocitiesfortwotrialshorizontal(top)andtwotrialsvertical(bottom)

• TheNationalScienceFoundation• TheJointInstituteofComputationalSciences• RezaAbiri andSoheil Borhani• Dr.Kwai Wong• XSEDEandSanDiegoSupercomputerCenter

EEGRegressionModelforBCICursorControl

Introduction

Students:JustinKilmarx,DavidSaffo,LucienNgMentor:Xiaopeng Zhao

Acknowledgements

TrainingProtocol

ModelDesign:Eachchannelwastestedindividuallyinourlinearregressionmodeltodeterminethemostimportantchannelsforvelocityprediction(Figure4).

Figure4:Heatmapofpredictionaccuracyforeachchannelduring5horizontaltrials

Cross-Validation:Themodelsweretestedwithtrialwisecrossvalidationwhere4trialswereusedfortrainingand1wasusedfortesting.Thiswasrepeatedforall5combinationsofhorizontalandverticaltrials.Thiswastoensurethemodelwiththebestaccuracyofvelocitypredictionwaschosenastheonetobeusedduringtheonlinecontrol.

Serialvs.Parallel:Aswearegeneratinganewmodelforeveryindividualsubjectandtrialwehavemanycomputationsthatareindependentofeachotherthatcanbedonefasterwithparallelprocessing.Doingthisalsoallowsustotestdifferentmodelsandhyper-parametersatoncereturningresultsfasterthanrunningeverythinginserial.WeusedDask Distributedtosetupournetworkwithaschedulerandworkernodes. ThenetworkwassetuponCometusing4nodes,96workers,4coresperworker.Timesareshownbelow.

ResultsAnEmotiv EPOC14-channelwirelessEEGheadsetwasusedtomeasurebrainwaveactivity(Figure3)whileBCI2000programrecordeddatasuchastheEEGactivityandcursorposition.AllofflineprocessingwasdoneusingPythonprogrammingontheCometsupercomputerinSanDiego.13previouspointsofEEGdatafrommemorywasusedasfeaturestotrainthemodels. Figure3:Channellocations

MaterialsandMethods

Serial AdaBoost 04:30:00Parallel AdaBoost 00:04:29

Features Horizontal Accuracy VerticalAccuracy

AllChannels 70.77% 44.67%

F7, 02,P8,T8,FC6,F4,F8,AF4 71.03% 41.68%

F7andF8 69.93% 25.64

F7,FC5,T8, FC6,F4,F8 72.73% 36.98%

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