eeg regression model for bci cursor controleeg headset was used to measure brainwave activity...
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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%