Amber Chao
Senior Thesis | 2026
AmberA.Chao
BostonUniversityAcademy,BostonUniversity
SeniorThesis
SrdjanDivac
January20,2026
Abstract
Thispaperpresentsanoverviewofandresearchutilizing theBrainTreebankdataset.BrainTreebankisadatasetof neurological,electroencephalographic(EEG)recordingstaken frommovie-watchingtestsubjects,providedbytheMITComputer ScienceandAILab.Thedatasethasasamplesizeof10subjects,a cumulativerecordingtimeof43hoursacross26movies,andis currentlythelargestdatasetofintracranialresolutionwith linguisticannotations(Wangetal.,2024).Thus,researchwas conductedtoassesstheusabilityofthenovelBrainTreebank datasetbyutilizingthedatasettoconductlanguagedecoding research.Naturallanguagedecodingisaprocessthatisimportant fordevelopingbrain-computerinterfacesandessentiallyconsists ofutilizingcomputationalmethodstodecipherspokenorheard wordsandsentencesfromneurologicalrecordings.Theresearch presentedinthispapersoughttodecodeheardlanguagefrom neurologicalrecordingsintheBrainTreebankdataset.Usingthe Pythonprogramminglanguage,thedatasetwasanalyzedforthe presenceofpreviouslyknownlinguisticneurologicalfeatures,such astheN400.Whiletheresultswerenotsignificantenoughto classifytheproducedmodelsasaccuratenaturallanguage decoders,themodelswereabletomorereliablyidentifybasic syntaxandsentencestructure(Wangetal.,2024).Therefore,these resultsdemonstrateBrainTreebank’susabilityforneurolinguistic researchandimpliesarequirementforamoreprecisedataset,a largerdataset,orbettermethodsofanalysistocreateatrue languagedecodingmachine.
Introduction
TheInfoLabattheMITComputerScienceandAILab (CSAIL)isonthecuttingedgeoflanguageresearchtopushthe boundariesofhuman-machinecommunication.Thenecessityof effectivecommunicationbetweenhumansandcomputershasonly grownasartificialprocessingcapabilityincreased,andthenext generationofsupercomputer-boundlargelanguagemodelsis alreadychallengingourbandwidthofcommunication(Jinetal., 2025;Tamkinetal.,2021).Inotherwords,ourabilityto comprehendandprocessthedataoflargelanguagemodelsisfar outstrippedbytheirsheermagnitude.Apossiblesolutioncould involvefacilitatingdirectcommunicationbetweenhumanand artificialbrainswithabrain-computerinterface.Theresearchin thispaperhasapplicationsinlanguage-basedbrain-computer interfaces,andispoisedtopushforwardscientificunderstanding ofthebrainaswellasitsdigitalcounterparts.However,thecore focusofthisresearchistobetterunderstandhumanlanguage.
Brain:OriginsandAdvancementsinNeurologyandBrain Mapping
Aslanguageisoneofthemostcomplexanduniquehuman adaptations,researchaimedatunderstandingithasnaturally focusedonthebrainovertime.Modelsofthehumanbrainhave evolvedasscientificunderstandinghasincreased,beginningwith basicmonolithicmodelswhichtreatedthebrainasasortof black-boxandexpandingtomorenuancedmodelsthatrecognized thebrainasbeingmadeupof(mostly)discretestructureswith specificpurposes(Hartwigsen,2024).Forinstance,inthelate19th century,PaulBrocaandCarlWernickediscoveredareas responsibleforlanguageproductionandlanguagecomprehension, respectively,bylocalizingareasofthebrainaffectedby stroke-inducedlesionsinpatientswithpatientswitheachsymptom (Hartwigsen,2024).Thus,researchintothelinguisticsignificance ofspecificbrainareaswasinitializedbyBroca,Wernickie,and
eventuallyGeschwind,andeachoftheirdiscoveredbrainareasare noweponymouslynamed(Hartwigsen,2024).
However,scientificunderstandingofthebrain’sstructures andfunctionsgreatlyincreasedwiththeadventofmorecomplex methodsofneuroimaging,suchasmagneticresonanceimaging (MRI),functionalmagneticresonanceimaging(fMRI),and magnetoencephalography(MEG).Magneticresonanceimagingis atechnologythatchieflymapsthedistributionandbondedstateof hydrogenatomsthroughoutavolume(Groveretal.,2015).Since areasofwhitematterandgreymatterinthebraindifferinfatand waterconcentrations,theyprovideMRIimagesofexcellent contrast(Langkammeretal.,2012).Giventheseproperties,MRI hasenabledscientiststogeneratethree-dimensionalmapsofbrains invivo,apossibilityunthinkabletoBrocaandWernicke. FunctionalMRIshrinksthesampletimeofconventionalMRIto withinanorderofmagnitudeofonesecond,allowingreal-time monitoringofbrainfunctions(Huotarietal.,2019).Thus,fMRIis mostusefulfortrackingtheflowofbloodandcompoundswithin thebloodthroughthebrain,suchashemoglobin(Huotarietal., 2019).AsMRIcandistinguishoxygenatedanddeoxygenated hemoglobin,fMRIcandetectwhichareasofthebrainareactive duringdifferentactivitiesbasedontheirconsumptionofoxygen (Huotarietal.,2019).Magnetoencephalographyisaprocesswhich directlymeasuresthestrengthofmagneticfieldsthroughthe volumeofthebraintopinpointneuronalactivity.Assuch,MEGis wellsuitedtolocalizingsourcesofbrainactivity,especiallythose causedbyseizures(Baillet,2017).WhileMEGisthemostcapable technologyofthethreeforstudyingcognitiveprocesses,allthree fallshortwhencomparedtoelectroencephalography(EEG) (Arjoonsinghetal.,2024).
Electroencephalographyisamethodtodirectlymeasure theelectricalimpulsesgeneratedbytheactivityofneuronswithin thebrain.Thisisincrediblyusefulfortheresearchbeing
conductedwithinthispaperbecauselinguisticconstructssuchas semantics,phonology,andsyntaxaremostpresentwithinthebrain asintracranialfieldpotentials(IFPs)(Hartwigsen,2024).1 Foran exampleofsemanticsignificanceinneurologicalfunctions, scientistsusedextracranialelectroencephalographytodiscoverthe N400,anegativevoltagespikethatoftenoccursinthebrain400ms afterthepresentationofanunexpectedorsemantically-violatory word(Hartwigsen,2024).Whilemorebasicandcommonly-used methodsofextracranialscalpEEGareusefulformeasuring generalpatternsinbrainfunctionwithhightemporalresolution, theylackthespatialresolutiontoisolateandlocatespecificsignals throughoutthebrain(Parvizi&Kastner,2018).Thus,thenext evolutionisstereo-electroencephalography(sEEG);sEEGutilizes intracranialelectrodes,whichcanachievesignal-to-noiseratiosup to100timesbetterthanscalpEEGbybypassingconduction throughtheepidermisandskeletonaswellasahigherdegreeof localizationduetosmallerandmoreevenlyspreadelectrode patches(Parvizi&Kastner,2018).Thankstothistwo-foldbenefit, brainsignalsofhigherqualitycannowbeacquired(Parvizi& Kastner,2018).UsingthebenefitsofsEEG,thisstudyaimsto furthertheneurologicalunderstandingoflanguageprocessing.
Treebank:AToolofLinguisticResearch
Oneofthemaintoolsofcomputationallinguisticsisa treebank,whichisadatastructurebestsuitedtothestorageand analysisofsemanticandsyntacticdata.TheUniversal Dependenciesprojectmaintainsoneofthelargest,most multilingualsetsofsyntactictreebanks,afactwhichlendsgreat credibilitytotheirprotocolsforannotation(Universal Dependencies,n.d.).Thus,tocreateadataformatsuitablefor neurolinguisticresearch,theresearchersatMITcreatedBrain Treebank:acollectionofneurologicalrecordingsand
1 Thevoltagescreatedbyneuronactivityinthebrain.
accompanyingEnglishwords,annotatedintheUniversal Dependenciesstyle.
Researchersbehindthelabforwhichthisstudywas conductedcollectedsEEGrecordingsfrommovie-watchingtest subjects.Thecollectedneurologicaldatawasannotatedwith myriadfeaturesfromthemoviethatpertainedtonaturalspeech processingsuchassentenceonsets,wordsurprisal,andpartof speech.Specifically,theannotationsfollowtheguidelinesofthe UniversalDependenciesprojectfortreebankstomaximizethe usabilityoftheneurologicaldataforlinguisticresearch.Thus,the datawasthenpackagedintoBrainTreebank,thehighestspatial resolution,linguistically-annotatedneurologicaldataset.Thisstudy aimstovalidatetheBrainTreebankdatabaseforitsaccuracyand applicabilitytonaturalspeechprocessingaswellasto preliminarilyanalyzethedatabasefornaturallanguagedecoding.
Methods
Thispaper’sresearchwasconductedutilizingdata exclusivelyfromtheBrainTreebankdatabase.TheBrainTreebank datasetwascollectedbytheInfoLabgroupattheMITComputer ScienceandAILabinconjunctionwiththeMITCenterforBrains, Minds,andMachines.BrainTreebankis,atthetimeofwriting,the largestlinguistically-annotated,stereo-electroencephalographic database(Wangetal.,2024).Thedatasetwaspublishedin2024 andwascollatedoveranundeterminedperiodoftimeprior(Wang etal.,2024).Thestudyfromwhichthedatasetwasgatheredwas conductedattheBostonChildren’sHospital(BCH)andproceeded withtheapprovalofBCHandtheHarvardIRB(Wangetal., 2024).Additionally,theclinicalresearchdataofthestudywas collectedwiththeinformedconsentofeachparticipant(Wanget al.,2024).
The10participantsofthestudyrangedfrom4to19years ofageandalreadyhadsEEGelectrodesimplantedforseizure localization(Wangetal.,2024).Eachparticipantviewed
full-length,Hollywoodmoviesona39.1centimeter,2880by1800 pixellaptopscreenplaced60to100centimetersawayfromthe participant(Wangetal.,2024).Movieswereplayedusinga customMatLabvideoplayertoensureconstantframerateplayback andaccuratesynchronizationsignalswiththeneuralrecorder (Wangetal.,2024).Additionally,movieswerere-renderedat 23.976fpstoprovidethesaidconstantframeratesourcematerial (Wangetal.,2024).Participantswerefreelyallowedtochoose moviesattheirowndiscretion,pause,resume,adjustvolume,and changepositions(Wangetal.,2024).However,participantswere askedtofocusontheplayedmoviestomaximizetheamountof usefulnatural-language-processingbraindata(Wangetal.,2024). Forthesamepurpose,participantsdidnotspeakduringthetrial andwerenotexposedtoalternate,externalsamplesofspeech whilethemoviewasplaying(Wangetal.,2024).Theexperiment proctorpausedandresumedthemovieasneededtoensurethe participant’sfullattentionwasgiventotheentiretyofeachmovie (Wangetal.,2024).
Theintracranialelectrodesutilizedinthisstudywere between21-56mmlongand0.8mmindiameter(Wangetal., 2024).Eachelectrodehadbetween6and16distinctcontact electrodesmeasuring2mminlengtheachwith1.5mmspacing betweeneachcontact(Wangetal.,2024).Thelocationatwhich eachsEEGprobewasimplantedwasdeterminedsolelybythe clinicalconsiderationsforseizurelocalization(Wangetal.,2024). Priortorecordingneurologicaldata,allparticipantsunderwenta oneTeslaMRIscantorecordthelocationsofthemetallic electrodepatches(Wangetal.,2024).FreeSurferwasthenutilized toremoveartifactsfromtheskullandparceltheelectrodesonto differentareasoftheDesikan-Killianybrainatlas(Wangetal., 2024).Thelocalfieldpotentialsmeasuredbytheprobeswere capturedwithamulti-channelneuralrecorderat2048Hz(Wanget al.,2024).Also,triggersignalsweresentfromthemovie-playing
laptopusingaUSBtriggerboxtosendcoordinatingpulsesevery 100ms,allowingtheextremelyprecisesynchronizationofmovie andneuraldata(Wangetal.,2024).Nonfunctionalelectrodes whichproducedexcessivelynoisydataforlongperiodsoftime werealsomanuallyremovedfromthedataset(Wangetal.,2024). Theresultantneuralrecordingwasscrubbedofidentifying informationsuchasictalbrainactivityandsavedtoanHDF5file (Wangetal.,2024).
Themovieswatchedbysubjectswereprimarilyannotated withtheirlinguisticfeatures,butauditoryandvisualfeatureswere alsocomputedandrecordedassupplementarydatatoavoidfalse correlations(Wangetal.,2024).Forthelinguisticfeatures,each movie’sclosed-captionsweresavedalongsidethemovietimeand splitupintoindividualwordsthatweremanuallyadjustedtoalign eachwords’onsetandoffset(exactstartandend)inthemovie (Wangetal.,2024).Stanford’spythonnaturallanguagetoolkit, calledStanza,wasfurtherusedtoparsethetranscriptofeach movietoannotatewords’partofspeechandpositioninasentence (Wangetal.,2024).Eachwords’surprisal2 wasalsocalculated usingtheGPT-2largelanguagemodel(Wangetal.,2024).Forthe non-linguisticfeatures,500mswindowsbeforeandaftereach words’onsetwereanalysedfortheirauditoryandvisualproperties (Wangetal.,2024).Auditorypropertiesincludedtheaverage pitch,changeinpitch,averagevolume,andchangeinvolumefor eachword(Wangetal.,2024).Visualfeaturesincludedthescreen brightness,numberofonscreenfaces,globalopticalflow magnitudeandangle3,andtheregularopticalflowmagnitudeand
2 Ametricwhichencapsulateshowunexpectedagivenwordisin thecontextofthesurroundingtext.
3 Ametricwhichrepresentstheamountanddirectioninwhichthe movie’scameramovesoveracertainclip.
angle4 (Wangetal.,2024).Formorequalitativevisualfeatures, cutsinthemovieweredetectedusingthePySceneDetectlibrary andeachdistinctscenewasreferencedagainstthePlaces365 datasettoproducearoughdescriptionofeachscene’slocation (Wangetal.,2024).Additionally,thespeakerofeachwordwas manuallyannotatedinthedatasetbytrainedannotatorswith specificcaretoincludeeverynameaspeakermayhave, irrespectiveofthechronologicalprogressionofthemovie(Wang etal.,2024).Whenreviewedaftercollection,thedataappearedas listsofallthewordsspokeninamoviewiththeexacttimeat whichtheywereeachspoken,alongwiththeirlinguistic,auditory, andvisualfeatures.
ResultsandDiscussion
ThisstudyshowedthattheBrainTreebankdatabasewas accurateforlanguagedecodingasotherlinguisticfeatures,suchas theN400andsentenceonsets,whichwereeasilydetectablein brainwaves.However,conclusiveresultssurroundingthe effectivenessofdecodingspeechfromneurologicalsignalscould notbeobtained–thedatayieldedalotofmixedresults.For example,linearmodelsprovidedmediocrecorrelationsbetween partsofspeechandneurologicalactivity,therebymakingitnearly impossibletodecodethepartofspeechofawordspokenata givenmovementintimefromtheneurologicaldata,muchlessthe worditself.
Thispaper’sresearchontheBrainTreebankdatabasewas conductedentirelyinthePythonprogramminglanguage.Thedata analystwasfamiliarizedwiththedatastructureandanalysis methodologythroughtheJupyterNotebookquickstartfile.All filenameswerepredicatedwiththeanonymizedsubjectidandtrial
4 Ametricwhichrepresentstheamountanddirectioninwhich objectsonscreenmovewithrespecttotheirbackgroundovera certainclip.
number,witheachtrialcontainingthedataofonlyonemovie viewing.The~.h5filescontainneuralrecordingsandthe ~timings.csvfilecontainsthesynchronizationoffsetsbetweenthe recordingsandabsolutetime(i.e.thewallclock).The ~metadata.csvfilecontainsinformationaboutthemovie,suchas thetitle,itslength,anditsmovieID(aninternalmarkerusedto differentiatebetweenmovies).The~features.csvcontainsthe transcriptofthemoviesaswellasthefeaturessurroundingeach word.The~electrode_labels.csvfilecontainsdescriptionsofthe electrodepositionspairedwithelectrodeids.Thegeneralanalysis methodologyisasfollows:Usingtheh5pylibrary,theneuraldata wasimportedintoanarrayof n rows,where n isthenumberof recordedelectrodes.Eachrowcontainsanun-timestampedarrayof voltagesthelengthoftherecording.Thenthetriggerfilewas importedusingthePandaslibraryintoanarray,whichcontained thetypeoftrigger,timeinthemovieatwhichthetriggeroccurred, thestartandendtimeofthetriggerpulseinabsolutetime,the indexoftheneuralsamplerecordedatthatcertaintrigger,andthe differencebetweenthecurrentandnextindex.Then,the features.csvfilewasimportedthesamewayasthetriggerfile, producinganarrayof42columns.Thefirstcolumncontained everywordspokenwithinthemovie,twoothercolumnscontained thestartandendofeachwordinmovietime,andtheother39 columnscontainedallthepreviously-mentionedfeaturesofthe BrainTreebankdataset.
Afterimportingallthedata,theneuralrecordingarraywas convertedintoaNumPyarray(fromtheNumPylibrary)to improvethenumberofavailablefunctionsandmakethedatamore easilymanipulable.Afterwhich,thearrayofneuralrecordingswas thenfilteredtoremoveinterferenceusingtheSciPylibrary.A notchfiltersetto60Hzandthecommonharmonicsof60Hzwas appliedtoremoveanynoisecausedbythelowfrequency, alternatingcurrentofpowertransmission.Then,theonsetofeach
11 wordwasalignedtoasampleindexoftheneuralrecordingusing themovietimeofeachwordandsynchronizationtriggerfile.After which,anarrayofeachwordandtheneuraldatasurroundingthe occurrenceofthatwordwasgenerated.Fortheremainderofthis paper,theseelementswillbereferredtoas‘wordwindows,’asitis notatedinthequickstart.Thesewordwindowscontainthe neurologicaldatasurroundingeachwordofthemovieanddefine thechunksizeofneurologicaldataanalyzedforallfutureanalysis.
Themainneurolinguisticanalysissoughttoextract evidenceoftheN400fromtheBrainTreebankdata.TheN400isa prominentevent-relatedpotential(ERP)withastatusasakey standardorcontrolforverifyingsemanticreactivityinthebrain (Kutas&Federmeier,2011).WhiletheN400wasinitiallythought tobeapartoftheP300ERP,whichcorrespondstothecognitive processofdecisionmaking,theN400waseventuallydistinguished asfollowingthepresentationofunexpected,semantically-violatory words(Shainetal.,1980).However,furtherresearchhas concludedthattheN400isactuallyamoregeneralresponseand partoflanguageprocessing,andthatthesurprisalofthepresented wordsimplymodulatestheN400(Arbeletal.,2010).
Surprisalisakeymetricforanalyzingthebehaviorofthe N400.Whilesurprisalcanberesearchedwithphysicalstudies utilizingclozetests5,thescaleoflanguagesamplesrecordedinthe BrainTreebankaswellasthewidespreadexposuretothemovies usedinthedatasetmadehumantrialsanincrediblyunappealing option(Nair&Oh,2026).Therefore,usinglarge-languagemodels (LLMs)tocalculatesurprisalwasanobvioussolution.LLMs providethemostprobablefollowingwordgivenacertain contextualinput(Nair&Oh,2026).However,itisalsopossibleto runtheinverseoperationtocalculatehowprobableagivenwordis
5Anexperimentwherewordsareomittedfromcompletesentences andparticipantsareaskedtoprovideexpectablereplacements.
tofollowacertaincontext.But,becausetheprobabilityofany singlewordfollowinganysinglecontextisexceedinglysmall, giventheextensivecorpusoftheEnglishlanguage,andnever exceedsone(a100%probability),surprisalispresentedasthe negativelogofprobability(Nair&Oh,2026).Themoreprobable awordis,thecloseritssurprisalistozero,andthelessprobablea wordis,thecloseritssurprisalstraystowardinfinity.
However,theexactrelationshipbetweensurprisaland N400amplitudeisunknown,giventhevariabilityofthebrainand theunknownpowerofthelog.Thus,theresultsofN400analysis resultshadtobemanuallyanalyzedforvisualcorrelations.Within theN400analysis,thedataanalystmeasuredneuralresponse400 millisecondsafterwordoffsetandcomparedtheinstantaneous voltageat100mstotheaveragevoltageofeachwordwindow, excludingthespike.Bydividingthespikevoltagebytheaverage voltage,theanalystmeasuredavoltagedifferencefrombaseline. Next,thecorrelationbetweenthe100msvoltagedifferenceandthe wordsurprisalwasmeasured.Directlycomparingthemagnitudeof theN400voltagespiketothewordsurprisalisquitedifficult becausefurtherresearchisneededtounderstandtheexact correlationbetweenthetwofactors.However,itisclearthata negativevoltagespikeispresentintheplaceoftheN400after wordswithhighsurprisal.Therefore,thedataanalystcreateda3D graphwithsurprisal,neuralactivity,andthetimewindowbefore andafterwhentheN400voltagespikeshouldhaveoccurred.That producedaprettysignificantresponse,meaninganoticeablespike overanorderofmagnitudegreaterthanthesurroundingareaof data.TheeasewithwhichtheN400wasresolvedfromtheBrain Treebankdatasetisstrongevidencetowardthedataset’sviability forstudyingpreviously-knownlinguisticneurologicalfeatures,as wellasevendeterminingaroughlocationofsaidlinguistic neurologicalfeatures.Therefore,givenhoweasilyfeaturessuchas theN400aredetectablewithintheBrainTreebankdataset,these
resultshighlysuggestthatother,morecomplexlinguisticanalyses shouldbepossiblewiththesamedata.
Conclusion
Theresearchinthispaperaimedchieflyatreviewingthe BrainTreebankdataset’susabilityforneurologicallinguistic researchand,secondarily,atpursuingneurolinguisticresearch, utilizingtheBrainTreebankdatasetitself.Theresearcherreviewed thepracticesofdatacollection,formatting,andannotationwhich wereemployedintheproductionoftheBrainTreebankdatasetand foundnomajorflawswithinthemethodsandproceduresused.
Next,theresearcherfollowedtheBrainTreebankdataset’s guidelinesfordataanalysisbydownloadingcopiesofthedataset andrunningdataingestiontestsinPython.Preliminaryanalysis withthegoalofidentifyingwellestablishedneurologicallinguistic featureswasconductedandprovedtobeverysuccessful;theN400 waseasilycorrelatedwithvoltagepeaksfollowingsemantically violatorywords.Thus,withthepresenceofpreexistent neurolinguisticfeatures,thereisahighlikelihoodthatother neurolinguisticfeaturesorevenlanguagedecodingcouldbe researchedusingtheBrainTreebankdataset.
Limitations
Thelimitationspresentintheexperimentofthisresearch paperaremany,andtheresultspresentedinthispaperarefarfrom exhaustive.Someexamplesofencounteredlimitationsinclude hardwarelimitations,experimentallimitations,andenvironmental limitations.
Asfarascomputationallimitations,alldataanalysisand processingwasconductedonaneighth-generationIntelprocessor, specificallyan8-coremobileI5-8350Uwith16GBofRAM. Accesstocomputersofgreatercorecounts,moreextensive processmemory,orevengraphicscardhardwareacceleration, couldhaveopenedfurtheravenuesforresearchwithlowertime andopportunitycosts.
Additionally,electrodeplacementwasoutsideofthe controlofthestudybecausetheelectrodeswereplacedwith considerationforandseizurelocalizationonly.Theelectrode placement,inessence,wasrandom,whichcausedasignificant portionofthedatatohavelowrelevancetothegoalofthestudy.
Anotherpossiblelimitationcouldbethelowsubjectcount andnarrowsubjectdemographic.Thevolumesofinformationthat arerequiredtotrainmachinelearningmodels,especiallylarge languagemodelsfareclipsedtheamountofdatathatwasanalyzed inthisstudy. Whilenotmuchisknownabouttheparticipantsin theBrainTreebankclinicalstudy,thejuvenileandhighly-plastic natureofthebrainattheagerangesofsaidsubjectscouldproveto beaseverelimitingfactor.Furtherstudieswithmoreparticipants ofmorewidelyvaryingages,couldprovidemoregeneralizable dataforamachinelearningmodel.
Afurthersimilarlimitationisthemonolinguisticnatureof theBrainTreebankdataset.Allparticipantsspoke,understood,and werepresentedmoviesinEnglishwhichcausedtheresultantBrain Treebankdataset,andthereforethisstudy,tohavelow internationalapplicability.Furthermore,BrainTreebank’s monolinguisticnatureprecludesotherinterestingresearchonthe natureoflanguageandcognitionasmultilingualanalysiswouldbe required.
Anotherconsiderationisthevariabilityofthemediathat waspresentedtotheparticipants.Whilehighmediavariability enlargeddatasetscanhelpmachinelearningmodelsdevelopmore appropriateandgeneralresponses,italsoaddsanadditional independentvariable.Theadditionofsuchindependentvariables makesdrawingdefinitescientificconclusionsfromthedataset moredifficult.
Lastly,thereisadistinct possibilitynoisefromavarietyof sourcescouldcontinuetopersistwithintheBrainTreebank dataset.Astheintracranialfieldpotentialsrecordedinthestudy
areminisculecomparedtotheelectricalcurrentscommonlyused inallpowerelectronics,thereisapossibilitythatadditional sourcesforinterferencemaynothavebeenaccountedfor, especiallygiventherecordingenvironment’surbanlocationand thepresenceofotherhigh-poweredmedicalequipment,suchas x-raysandMRImachines.
FutureResearch
Aworthwhilegoalforfutureresearchwouldbetoexpand BrainTreebanktoencompassagreaternumberoflanguages,inthe samewaytheUniversalDependency’sTreebankdoes.Thatway, moregeneralconclusionscouldbedrawn,suchasthewayhuman languageprocessingoccursasawholeandthewaysemantic conceptsareprocessedandunderstoodinthebrain.
Futurestudiesshouldaimtoconcentrateelectrodesinthe temporallobetomaximizetheamountoflinguistically-relevant datacollected.However,withthevariablestructureandfunctionof differentindividuals’brains,itisimpossibletoknowforcertain wherethemostlinguistically-activeportionsofaperson’sbrain maybe.Therefore,agoodcourseofactionforfuturestudiescould betotakeinitialrecordingsfromallpartsofanindividual’sbrain, conductsimplelinearanalyses,ascertainareastofocuson,and re-recordneuraldatawiththesamestimulusinamore tightly-monitoredareaofthebrain.
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