Progress in Brain Computer Interface: Challenges and ...

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. ThisarticleispartoftheResearchTopic CognitiveNeurodynamics:IntegratingCognitiveScienceandBrainDynamics Viewall 15 Articles Articles AlessandroE.Villa Neuro-heuristicResearchGroup(NHRG),Switzerland DongruiWu HuazhongUniversityofScienceandTechnology,China DavidR.Painter TheUniversityofQueensland,Australia Theeditorandreviewers'affiliationsarethelatestprovidedontheirLoopresearchprofilesandmaynotreflecttheirsituationatthetimeofreview. Abstract 1.Introduction 2.Challenges 3.Neuroplasticity,Sensors,SignalProcessing,Modeling,andApplications 4.EthicalConcernsandSocioeconomicContexts 5.Conclusion AuthorContributions ConflictofInterest Acknowledgments References SuggestaResearchTopic> DownloadArticle DownloadPDF ReadCube EPUB XML(NLM) Supplementary Material Exportcitation EndNote ReferenceManager SimpleTEXTfile BibTex totalviews ViewArticleImpact SuggestaResearchTopic> SHAREON OpenSupplementalData REVIEWarticle Front.Syst.Neurosci.,25February2021 |https://doi.org/10.3389/fnsys.2021.578875 ProgressinBrainComputerInterface:ChallengesandOpportunities SimantoSaha1,2*,KhondakerA.Mamun3,KhawzaAhmed2,RaqibulMostafa2,GaneshR.Naik4,SamDarvishi1,AhsanH.Khandoker5andMathiasBaumert1* 1SchoolofElectricalandElectronicEngineering,TheUniversityofAdelaide,Adelaide,SA,Australia 2DepartmentofElectricalandElectronicEngineering,UnitedInternationalUniversity,Dhaka,Bangladesh 3AdvancedIntelligentMultidisciplinarySystems(AIMS)Lab,DepartmentofComputerScienceandEngineering,UnitedInternationalUniversity,Dhaka,Bangladesh 4AdelaideInstituteforSleepHealth,CollegeofMedicineandPublicHealth,FlindersUniversity,Adelaide,SA,Australia 5HealthcareEngineeringInnovationCenter,DepartmentofBiomedicalEngineering,KhalifaUniversityofScienceandTechnology,AbuDhabi,UnitedArabEmirates Braincomputerinterfaces(BCI)provideadirectcommunicationlinkbetweenthebrainandacomputerorotherexternaldevices.Theyofferanextendeddegreeoffreedomeitherbystrengtheningorbysubstitutinghumanperipheralworkingcapacityandhavepotentialapplicationsinvariousfieldssuchasrehabilitation,affectivecomputing,robotics,gaming,andneuroscience.Significantresearcheffortsonaglobalscalehavedeliveredcommonplatformsfortechnologystandardizationandhelptacklehighlycomplexandnon-linearbraindynamicsandrelatedfeatureextractionandclassificationchallenges.Time-variantpsycho-neurophysiologicalfluctuationsandtheirimpactonbrainsignalsimposeanotherchallengeforBCIresearcherstotransformthetechnologyfromlaboratoryexperimentstoplug-and-playdailylife.Thisreviewsummarizesstate-of-the-artprogressintheBCIfieldoverthelastdecadesandhighlightscriticalchallenges. 1.Introduction Thebraincomputerinterface(BCI)isadirectandsometimesbidirectionalcommunicationtie-upbetweenthebrainandacomputeroranexternaldevice,whichinvolvesnomuscularstimulation.Ithasshownpromiseforrehabilitatingsubjectswithmotorimpairmentsaswellasforaugmentinghumanworkingcapacityeitherphysicallyorcognitively(LebedevandNicolelis,2017;SahaandBaumert,2020).BCIwashistoricallyenvisionedasapotentialtechnologyforaugmenting/replacingexistingneuralrehabilitationsorservingassistivedevicescontrolleddirectlybythebrain(Vidal,1973;Birbaumeretal.,1999;Alcaide-Aguirreetal.,2017;Shahriarietal.,2019).Thefirstsystematicattempttoimplementanelectroencephalogram(EEG)-basedBCIwasmadebyJ.J.Vidalin1973,whorecordedtheevokedelectricalactivityofthecerebralcortexfromtheintactskullusingEEG(Vidal,1973),anon-invasivetechniquefirststudiedinhumansinventedbyBerger(1929).AnotherearlyendeavortoestablishdirectcommunicationbetweenacomputerandthebrainofpeoplewithseveremotorimpairmentshadutilizedP300,aneventrelatedbrainpotential(FarwellandDonchin,1988).Asanalternativetoconventionaltherapeuticrehabilitationformotorimpairments,BCItechnologyhelpstoartificiallyaugmentorre-excitesynapticplasticityinaffectedneuralcircuits.Byexploitingundamagedcognitiveandemotionalfunctions,BCIaimsatre-establishingthelinkbetweenthebrainandanimpairedperipheralsite(Vansteenseletal.,2016).However,theresearchapplicationsofBCItechnologyevolvedsignificantlyovertheyears,includingbrainfingerprintingforliedetection(Farwelletal.,2014),detectingdrowsinessforimprovinghumanworkingperformances(Aricòetal.,2016;Weietal.,2018),estimatingreactiontime(Wuetal.,2017b),controllingvirtualreality(Vourvopoulosetal.,2019),quadcopters(LaFleuretal.,2013)andvideogames(Singhetal.,2020),anddrivinghumanoidrobots(ChoiandJo,2013;Spataroetal.,2017).Figure1demonstratestheprogressionofBCIinvariousapplicationfieldssinceitsconception. FIGURE1 Figure1.Thenumberofpublicationsovertheyears:ThestatisticswasbasedonasearchonPubMedinwhich“braincomputerinterface”wasthesearchkeyword.Thepublicationsthosewerelisteduntil4thDecember2020havebeenaccountedonly.Asignificantincreaseinthenumberofpublicationsinthisdecadeascomparedtothelastdecadeimplicatestheengagementofagreatercommunityinthisfieldand,thustheimportanceofBCItechnology. AccordingtotheBrain/NeuralComputerInteractionHorizon2020project,aninitiativebytheEuropeanCommissionforcoordinatingBCIresearch,sixmajorapplicationthemes,i.e.,restore(e.g.,unlockingthecompletelylocked-in),replace(e.g.,BCI-controlledneuroprosthesis),enhance(e.g.,enhanceduserexperienceincomputergames),supplement(e.g.,augmentedrealityglasses),improve(e.g.,upperlimbrehabilitationafterstroke),andresearchtool(e.g.,decodingbrainactivitywithreal-timefeedback)havebeenoutlinedasfeasibleandpromisingfields(Brunneretal.,2015).ThisoverviewencompassesawiderangeofchallengesandtrendsinBCIfield.ForspecializedreviewsonparticularBCItopicswerefertotherecentliterature(McFarlandetal.,2006;Schwartzetal.,2006;Bashashatietal.,2007;Lotteetal.,2007,2018;Matthewsetal.,2007;Sitarametal.,2007;MakandWolpaw,2009;Menonetal.,2009;NicolelisandLebedev,2009;Summereretal.,2009;VaadiaandBirbaumer,2009;MilanandCarmena,2010;Minetal.,2010;Clausen,2011;Krusienskietal.,2011;Liaoetal.,2012;Nicolas-AlonsoandGomez-Gil,2012;Shihetal.,2012;Jebari,2013;McCullaghetal.,2014;AhnandJun,2015;Jayarametal.,2016;LebedevandNicolelis,2017;Mudgaletal.,2020;Rashidetal.,2020;SahaandBaumert,2020). 1.1.CharacterizationofBCISystems BCIsystemscanbecategorizedbythewaytheyusethebrain:PassiveBCIdecodeunintentionalaffective/cognitivestatesofthebrain(Zanderetal.,2009),whileactiveBCIdirectlyinvolvetheuser'svoluntaryintention-inducedbrainactivity.ReactiveBCIusebrainwavesgeneratedasresponsetoexternalstimuli.Detectingdriver'sdrowsinesstopreventroadaccidentsisanexampleofpassiveBCI(Linetal.,2008;Gaoetal.,2019).BCIsystemsdrivenbyusers'intentionalmotorimagery(MI)(Marchesottietal.,2016;Sahaetal.,2019a;SahaandBaumert,2020)andvisuallyevokedP300producedbyexternalstimulation(Farwelletal.,2014)canbeconsideredactiveBCIandreactiveBCI,respectively. Themodalityofsignalacquisitionhasbeenusedtodividesystemsintoinvasiveandnon-invasiveBCI(Minetal.,2010;RosenfeldandWong,2017).Non-invasiveBCIexploitingEEGaremostcommon,althoughmorerecently,functionalnearinfraredspectroscopy(fNIRS)(Matthewsetal.,2007),magnetoencephalography(MEG)(Fukumaetal.,2016),functionalmagneticresonanceimaging(fMRI)(Kaasetal.,2019)andfunctionaltranscranialDopplerultrasonography(FaressandChau,2013;Luetal.,2015;Khalafetal.,2019)havebeenexploited.Incontrast,invasiveintracorticalelectrodes(Pandarinathetal.,2017)andelectrocorticography(ECoG)(Kaijuetal.,2017)havebeenused,providingasuperiorsignal-to-noiseratioandbetterlocalizationofbrainactivity.Table1summarizessignalacquisitionmodalitiesandtheirsuitabilityforBCIapplications. TABLE1 Table1.Alistofneuroimagingtechniquesandtheirsuitabilityinbraincomputerinterface(BCI)applications. Recenttechnologicaladvancementsallowboththedecodingofneuralactivitiesandthedeliveryofexternalsignalsintotargetedbrainareastoinduceplasticity,i.e.,remodelingofneurosynapticorganization(Lajoieetal.,2017).PlasticityisaninherentcharacteristicofthebrainandperipheralnervoussystemunderpinningBCI-basedrehabilitationandotherneuroscientificapplications.WhilemostoftheBCIsystemstranslatebrainsignalstocomputercommands,somesystemsutilizeexternalstimulationmodalitiessuchastranscranialmagneticstimulation(Grauetal.,2014;Raoetal.,2014;Schaworonkowetal.,2019)andtranscranialdirectcurrentstimulation(Baxteretal.,2017)tostimulatespecificbrainareas.ThebidirectionalframeworkofBCIcompriseseitheronebrainwithfeedbackmodalityortwobrains.TranscranialdirectcurrentstimulationdirectedbyMI-relatedEEGsignalsalterstheconnectivityinsensorimotornetworksofhealthyindividuals(Baxteretal.,2017).AnotherpossibleapplicationofbidirectionalBCIframeworkisdirectbrain-to-braincommunication(Grauetal.,2014;Raoetal.,2014).Moreover,someBCIapplicationsrequireauxiliarymodalities,e.g.,proprioceptivefeedbackandfunctionalelectricalstimulationdrivenbybrainsignalsasfeedbackforaugmentingorregainingperipheralmotoractions(Darvishietal.,2017;Bhattacharyyaetal.,2019;Bockbraderetal.,2019;Murovecetal.,2020). 1.2.FactorsInfluencingBCIPerformance FormedicalapplicationsofBCI,threecriteriaareessential:(1)acomfortableandconvenientsignalacquisitiondevice,(2)systemvalidationanddissemination,and(3)reliabilityandpotentialityofBCI(Shihetal.,2012).Fortherestorationofmobilityinpatientswithmotorimpairments,invasiveintracorticalrecordingsshowbetterBCIperformance(Hochberg,2013)thannon-invasivemethodssuchasEEG(MilanandCarmena,2010).Theperformancedetermineshowefficientlyapatientcanperformanimpairedmotortaskorcommunicatewithanexternaldevice.Invasivemodalitiesarealsosuitableforlocked-inpatients,becausethebenefits(significantlyimprovedquality-of-life)outweightherisksassociatedwithimplantation(Giljaetal.,2011).Apilotstudyfoundnoadverseeffectspertainingtosurgeryortissuereactionat1yearfollow-up(Friehsetal.,2006).InvasiveBCIshouldgenerallynotbeconsideredforneurologicallyintactpeopleduetorisksassociatedwithsurgery.However,invasiverecordingsmayenabletheutilizationoflocalizedinnercortexactivitiesandabetterinterpretationofsurfacerecordingsfromanon-invasivemodality(Schalk,2010;Linaetal.,2012). ManyfactorsinfluenceBCIperformance;takingtheunderlyingcortical-subcorticalnetworksintoconsiderationisofcrucialimportance.Forexample,MI-inducedsignalsarebestrecordedfrompremotorandmotorareas,becausepremotorcortex,primarymotorcortexandsupplementarymotorareaalongwithbasalgangliaandthalamusofthesubcorticalareasarethemostlyactivatedareasduringMI(Marchesottietal.,2017).WhileEEGcancapturepremotorandmotorareaactivation(Edelmanetal.,2015;Sahaetal.,2019a),intracorticalelectrodescanrecordsignalsfrombasalgangliaandthalamus(Sandetal.,2017). SeveralissuescansignificantlyimpedeBCIperformance.Maintaininganacceptablesignal-to-noiseratioinnon-invasivelong-termrecordingsiscritical.Event-inducedbrainwavesoroscillationsaredynamicandaffectedbyunstablerestingstatenetworks(RSNs)(Mantinietal.,2007).Time-variantpsychophysiological(Gonçalvesetal.,2006;Zhangetal.,2015;Acqualagnaetal.,2016;SahaandBaumert,2020),neuroanatomical(Kasaharaetal.,2015)factorsandusers'fundamentaltraits(AhnandJun,2015)causeunreliableestimatesofRSNs,causingshortandlong-termsignalvariationwithinandacrossindividuals(SahaandBaumert,2020).Duetotheseintrinsicsignalvariations,BCIsystemsrequiresubject-specifictraining,duringwhichsubjectsattendacalibrationsessionthatistediousandoftenfrustrating.Toeliminatesubject-specifictraining,theconceptofinter-subjectassociativity,demonstratedinpreviousworksincaseofnaturalvision(Hassonetal.,2004)andnaturalmusiclistening(Abramsetal.,2013),couldbeexploitedtowardinter-subjectoperableBCI.Recentstudiessuggestthatinter-subjectoperablesensorimotorrhythm-basedBCImightbecomefeasibleforsubjectswhosharecommonbraindynamics(Sahaetal.,2017,2018,2019a;SahaandBaumert,2020).Inter-subjectBCIholdspromisepredominantlyforhealthypeopleandinapplicationssuchasgaming,drowsinessandliedetection,becauserehabilitativeBCImustconsiderthecharacteristicsandseverityofindividualimpairment(Parketal.,2016).Transferlearningcanalsoreducetheeffectsofsession-to-sessionandsubject-to-subjectvariabilities,byusingsystemsthatweretrainedondatafromdifferentpeopleexploitingcommonaltiesandreducingtrainingrequirements(Jayarametal.,2016;Sahaetal.,2017,2018,2019a;HeandWu,2019;Wuetal.,2020). 2.Challenges 2.1.PsychophysiologicalandNeurologicalChallenges Emotionalandmentalprocesses,neurophysiologyassociatedwithcognitionandneurologicalfactors,i.e.,functions,anatomy,playcrucialrolesinBCIperformanceandgiverisetosignificantintra-andinter-individualvariability(SahaandBaumert,2020).Psychologicalfactorssuchasattention,memoryload,fatigueandcompetingcognitiveprocesses(Gonçalvesetal.,2006;Käthneretal.,2014;CalhounandAdali,2016)aswellasusers'basiccharacteristicssuchaslifestyle,gender,andage,(Kasaharaetal.,2015)influenceinstantaneousbraindynamics.Forexample,individualswithlowerempathyparticipatelessemotionallyinaP300-BCIparadigmandcanproducehigheramplitudesofP300wavesthansubjectswithgreaterempatheticinvolvement(KleihandKübler,2013).MotivationisalsorelatedtoP300-BCIperformance(Nijboeretal.,2010). Besidespsychologicaltraits,restingstatephysiologicalparameters,forexample,frequencydomainfeaturesofrestingstateheartratevariabilityareassociatedwithBCIperformance(Kaufmannetal.,2012).Inaddition,thebaselinesofRSNsaredynamicandmodifyanycorticalsignatureinstantaneously(Mantinietal.,2007).AgealtersRSNsandassociatedcognitiveresponses(Wangetal.,2016b).Adaptingtosuchtime-variantRSNsismoredemandingwhentheeffectsofRSNsmaskevent-relatedcorticalresponses(Jensenetal.,2011).Moreover,theinherentcomplexityanddiversityintheformationofhumanbrains(Sporns,2013)thatinfluencethefunctionalneuralnetworks(Honeyetal.,2010),constructhighlyvolatileneuronalconnectivityovertimeandacrosssubjects(Honeyetal.,2009).AnefficientBCIsystemmustberobusttosuchinherentphysiologicalfluctuationsovertimetoenablemoregeneralizedsystems(SahaandBaumert,2020). ExperimentscorrelatingBCIperformancewithneuroanatomical,neurophysiologicalandpsychologicalparametershaveprovidedfascinatingresults:graymattervolumeinsensorimotorcorticalareasisassociatedwithBCIsuccess(Kasaharaetal.,2015).Sensorimotorrhythm-basedBCIhasimplicatedthatphysiologicalpredictorssuchasspectralentropyandpowerspectraldensity,derivedfromrestingstateEEGrecordingsarecorrelatedwithBCIperformance(Zhangetal.,2015;Acqualagnaetal.,2016).Psychologicalpredictorssuchasattentionandmotivation,arealsoassociatedwithsensorimotorrhythm-basedBCIperformance(Hammeretal.,2012).CorticospinalexcitabilitycouldbeusedasanotherreliablemarkerforBCIperformance(Vasilyevetal.,2017).TakingheadanatomyintoconsiderationaugmentsBCIperformance(Wronkiewiczetal.,2015;Sahaetal.,2019a). Around15–30%ofindividualsareinherentlynotabletoproducebrainsignalsrobustenoughtooperateaBCI(Blankertzetal.,2009;Halderetal.,2019;Cecotti,2020).ConsideringneurophysiologicalphenomenamayreduceBCIilliteracy.AnadaptivemachinelearningapproachincorporatingneurophysiologicalandpsychologicaltraitshasbeenproposedtoreduceBCIilliteracy(VidaurreandBlankertz,2010).ThecausesofBCIilliteracydonotexclusivelyrelyonusers'abilitytoproducesignals.SometimestechnologicallimitationsmayhinderessentialfeaturesextractionforasuccessfulBCIoperationforanindividual.Forexample,measurementsofscalpEEG/MEGmaynotshowgoodtask-specificsignalsduetothefoldingofthecortexorscalp-to-cortexdistanceforthatindividual(Andersenetal.,2020). Othercase-specificinvestigationsonneuro-psycho-physiologicalparameterscontributingtoBCIperformanceareessential.Fortherehabilitationofstrokesurvivors,affectedneuralcircuits,i.e.,lesionsaretobeidentifiedcarefully,becausebrainresponsesfluctuateaccordingtothespatiallocationofthestrokelesion(Parketal.,2016).Althoughcurrentneuroimagingmethodsareeffectiveincapturingstrokelesionsites,acase-specificBCIdesignthatincorporatesresidualbrainfunctionisrequiredforrehabilitativeinterventions.HighlyindividualizeddesignimpedeswidedisseminationofBCI-drivenrehabilitationofneurologicalconditions. 2.2.TechnologicalChallenges Eventrelatedpotential(ERP)(McCaneetal.,2015),steady-statevisualevokedpotential(SSVEP)(Chenetal.,2015;Abu-AlqumsanandPeer,2016),auditoryevokedpotential(AEP)(Schreuderetal.,2010),steady-statesomatosensoryevokedpotential(SSSEP)(Muller-Putzetal.,2006;Oxleyetal.,2017),andmotorimagery(MI)(Marchesottietal.,2016;Sahaetal.,2019a;SahaandBaumert,2020),havebeenproposedtodetectcognitivesignaturesalthoughnoneoftheapproachesperformswellforallBCIapplications.Forexample,ERPsandSSVEPsaretarget-specificandelicitedbyexternalstimuli;however,ifERPsdependonvisualstimuli,theycannotbeusedforcommunicationbylocked-inpatientswithimpairedvisualprocessing.Inthatcase,auditory-basedERP(e.g.,AEP)couldbeusedifauditoryprocessingremainsintact.TheSSVEPmethodprovidesthehighestinformationtransferrateofanon-invasiveEEG-basedBCI(Chenetal.,2015;Abu-AlqumsanandPeer,2016).LimitationsoftheSSVEPtechniqueincludevisualfatiguecausedbylookingataflickeringdisplayforalongtime.Whenusingthistechnique,thecontrolsignalcouldbearbitraryandcounter-intuitive,althoughitmightdependmostlyontheexperimentalcontext.Forexample,whenusingaBCIspellerbasedonSSVEP,anindividuallooksattheletter“A”,whichflickersat10Hz.Itisgenerallynotgivenimportancetoanyinherentrelationshipbetween“A”and10Hz,insteadthecontrolsignalisarbitrarymappedtointerfacewithacomputer.AnadvantageofanMI-basedBCIistheuseofexplicitmappingoftask-relatedbrainsignalstooperate(SahaandBaumert,2020).However,MIseemstooslowforactioncontrol,thustheyarenotsuitableforcontrollingvirtualrealityenvironmentsorvideogames(Lécuyeretal.,2008).RecentlyproposedhybridBCIswhichutilizemorethanonesignature,i.e.,SSVEP/ERP(CombazandVanHulle,2015;Yinetal.,2015)andSSVEP/MI(Pfurtschelleretal.,2010;Horkietal.,2011),seemtooffermorerobustfeatures.ConsideringasynchronousBCIwheretheuserdecidestoactivateacommandwhennecessary,theperformanceisstillunsatisfactory(Hanetal.,2020). TheintrinsicneurophysiologicalinstabilityofbraindynamicsposescriticalchallengesformakingBCIsystemsefficient.ThemajorcomponentsofaBCIsystemaresignalacquisition,signalprocessingandeffectordevice(Schwartzetal.,2006).Variousneuroimagingtechniqueshavebeenusedtoexplorecorticalactivitiesthrougheitherelectricalorhemodynamicsignatures(Minetal.,2010),butnoneofthemethodsshowsanyadvantageforalucrativeBCIdesignmeetingthefourimportantcriteria:costefficiency,portability,easymaintenance,andlittleornoinvolvementofsurgery.EEG-basedBCIarerelativelymorecompliantwiththeabovementionedcriteriaascomparedtoothersignalacquisitionmodalities.Tables2,3listadiverserangeofBCIapplicationsexploitingEEG.Bothinvasiveandnon-invasivesignalacquisitionshaverecentlyshownthatreliablelong-term(i.e.,foratleastseveralmonths)useofBCIsystemsisquitefeasible(Saeedietal.,2016;Sauter-Starceetal.,2019;Shahriarietal.,2019;Oxleyetal.,2020). TABLE2 Table2.Asummaryofsensorimotorrhythmsandelectroencephalography-basedbraincomputerinterface(BCI)studies. TABLE3 Table3.Asummaryofnon-sensorimotorrhythmsandelectroencephalography-basedbraincomputerinterface(BCI)studies. EEGprovidesrelativelypoorspatialresolutionduetonon-invasivescalprecordingscomparedtofMRI,butfinertemporalresolution(LystadandPollard,2009;Minetal.,2010;Heetal.,2011;Nicolas-AlonsoandGomez-Gil,2012).EmployinghighdensityEEGmappingincreasesspatialresolutionbutresultsinhighcomputationalcostandeffortstomaintainareasonablesignal-to-noiseratioacrossallchannels(Chenetal.,2015).SinceEEGcapturesonlytheelectricalfieldassociatedcognitiveprocesses,concomitantassessmentofblood-oxygenlevel-dependent(BOLD)activitymayimproveBCIperformance.BOLDactivityistypicallycapturedwithfMRI(Sitarametal.,2007),whichisnotfeasibleformostBCIapplications,duetounmanageablesizeandcostofthedevice.fNIRSprovidesasafe,non-invasive,relativelyinexpensiveandportableneuroimagingalternativeforrecordingBOLDactivity(Matthewsetal.,2007).IntegratingfNIRSwithEEGcansignificantlyenhanceclassificationperformancesregardlessoflowinformationtransferratecausedbyinherentdelaysinhemodynamics(Fazlietal.,2012).ArecentstudyhassuggestedthatfNIRSisunabletoadequatelyofferacceptableperformancesonitsown,butcanbecombinedwithEEGtoboosttheperformances(Geetal.,2017).However,continuoustechnologicaladvancescouldpromotefNIRSasanexclusivetoolforneuroscienceresearch,includingthedevelopmentofBCI(Scholkmannetal.,2014;NaseerandHong,2015). Probingsourcesincortico-subcorticalnetworksisanotherimportantlimitationofscalp-basedsensorssuchasEEG.Reconstructingtask-inducednetworkswhileresolvingtheso-calledinverseproblemimposesasignificantchallenge.Atwo-equivalent-dipolemodelwasappliedonEEGdatatodiscerntheanatomicalnatureoftheMIinducedsourcesandtoaidtheclassificationperformances(Kamousietal.,2005).Sahaetal.proposedawavelet-basedsourcelocalizationapproachtoinvestigateMI-relatedsourcesandtheirimpactonBCIperformance(Sahaetal.,2019a).Theneuronalpotentialsattenuatethroughseveraltissuelayersofcomplexgeometryanddiverseelectricalproperties;however,themagneticpermeabilityinthecerebrospinalfluid,skull,andskin,isconsistent(daSilva,2013).Thus,MEGcancapturesignalwithlessdistortionthanEEG.AlthoughMEGprovidesbetterspatiotemporalresolutionascomparedtoEEG,themagneticfieldcreatedbythebrainisverysmall,requiringcostly,stationaryrecordingequipment(Mellingeretal.,2007;Corsietal.,2019). TheBCIclassifierdesignhastoaddresstwoissues(Bashashatietal.,2007;Lotteetal.,2007,2018).First,thedimensionalityofthefeaturessetusedforestimatingthemodelparametersshouldbechosenforoptimalperformancebasedonthenatureoftheclassifier.Second,thetrade-offbetweenbiasandvariancehastobeconsideredandmayinvolveregularizingtheparameterestimation. Covariateshiftoccurswhenthefeaturesextractedfromthetrainingdifferfromthoseoftestdataimpactingtheclassificationperformance(Krusienskietal.,2011).Covariateshiftisanimportantissuerequiringtheapplicationofadaptivemethodsforcompensatingfeaturespacetransitions(Jayarametal.,2016;SahaandBaumert,2020).Theunsupervisedsubspacelearningmethodenablessession-to-sessionandsubject-to-subjectinformationtransfers,augmentingBCIperformance(Sameketal.,2013;Jayarametal.,2016;Sahaetal.,2018).Thecommonspatialpattern,asupervisedmethod,hasbeenextensivelyusedinEEG-basedonlineandofflineBCIsettings(Ramoseretal.,2000;Wuetal.,2017a).Acommonproblemwithsuchadata-driventechniqueisover-fittingofthemodelparametersbasedontrainingsets,causingunreliablepredictiononthetestdata(Sannellietal.,2016).RecentstudiesintegrateddiversemethodsintopotentialtransferlearningframeworksforBCIincludingspatialfilters(e.g.,commonspatialpattern),Riemanniangeometry,Euclideanalignmentandsubspaceadaptationanddeeplearning-basedtechniques(Barachantetal.,2011;Congedoetal.,2013,2017;Maratheetal.,2015;Wuetal.,2016,2017b;Wuetal.,2020;HeandWu,2019,2020;Kwonetal.,2019;ZhangandWu,2020). 3.Neuroplasticity,Sensors,SignalProcessing,Modeling,andApplications Exploitingneuroplasticity,designinghi-fidelityandcustomizedneuralsensors,applyingadvancedsignalprocessing,andmachinelearningtechniquesarethekeyaspectsofaneffectiveBCIdesign.Tables2–5highlightdiversecharacteristicsofBCIcomponentsandapplicationsincludingsignalacquisitionmodality,experimentalparadigm,dataanalysisandpatternrecognition,applicationfield,andsignificance.Notably,therearenospecificselectioncriteriaforstudiesinTables2–5duetothebroadspectrumoftopicscoveredinthisreview;however,theyaresummarizedsuchthatcriticaladvancesoverthelastseveralyearscanbeappreciated. 3.1.NeuroplasticityandCognitiveRehabilitation Thetime-variantbehaviorofsynapseswithincomplexneuralnetworksunderpinstheplasticcharacteristicsofthebrainandwasfirstillustratedbyDonaldO.Hebbin1949(BrownandMilner,2003).Neuroplasticitynotonlyhelpstoassistcognitiveandperceptuallearningbutalsoisthemainingredientforneurorehabilitation.Howplasticaparticularbrainareais,maydefinetheeffectivenessofaneurofeedbackstrategytoinducespecificactivitypatterns.Studieshaveshownvisualcorticesareplasticenoughtoproducerobustneuralsignalsforpost-neurofeedbackperceptuallearning(Shibataetal.,2011;Amanoetal.,2016).Anotherstudyhasdemonstratedifright/lefthemisphericdifferencesinneurofeedback-inducedalphaactivitiesareassociatedwithvisualinformationprocessingandmotorbehaviors,and,thus,controlspatialattention(JonesandSliva,2020).fMRI-basedneurofeedbacktrainingsessionsinducetheplasticityofattention-relatedbehavior.Implicationssuggestthatneurofeedbackcanofferrehabilitationofattentionaldeficit(Meganetal.,2015).Arecentstudyhasusedneurofeedbacktogeneraterobustsomatosensoryoscillationsassociatedwithhumanperception(Brickweddeetal.,2019). Closed-loopBCIwithneurofeedbackisassumedtocontributetothereorganizationofcortical-subcorticalneuralnetworksandassistsubjectsinself-regulatingspecificbrainrhythms;notwithstanding,theunderlyingmechanismsthatalterneuralsubstratesarestillnotfully-understood(Sitarametal.,2017).Forexample,BCI-basedcovertvisuomotortrainingmodulatesassociatedneuralsubstrates,wheretheeffectsofmodulatedneuralsubstratesareobservedwhileperformingthatparticularmovement-relatedtask(Vyasetal.,2018).Substantialchangesinovertmovement-relatedtaskfollowingBCI-driventraininginducedlearningsuggestacriticalroleofBCIinenhancedmotorlearningforproficientlycontrollingneuroprosthetics(Orsbornetal.,2014),i.e.,devicesthatcanenhanceorrepairtheoutputofthenervoussystem.Forexample,intracorticalelectrodesmaybeusedtostimulatespecificbrainregionstoregainmotorcontrol(Oxleyetal.,2016;Sandetal.,2017).BCImayaugmenttraining-inducedplasticityduringtherapeuticmotorrehabilitationand,thus,re-excitecorrespondingneuralsubstratestoregaincontrolbymeansofneuroprostheticsorupperlimbfunctions(Dobkin,2007).OtherexamplesincludeBCI-drivenexoskeletonstoenhancehumanworkingcapacity(Benabidetal.,2019). TheextentofBCI-inducedplasticityentailsseveralfactors,including(1)theselectionofthesignalacquisitionmodality,whichplaysanimportantroleindiagnosingneuralstates,(2)thedesignoffeedbackmodalitythathasexplicitassociationwiththeneuralsignalclassificationperformance,(3)theconsiderationofapplication-specificfeedbackdelays,and(4)theutilizationofasuitablefeedbackmodality(Grosse-Wentrupetal.,2011).NeuralensemblerecordingsusingsignalacquisitionmodalitiessuchasEEG,MEG,fNIRS,andfMRIhavebecomedominantoversingleunitrecordings.Behavioralactivitiesarelikelytobedistributedacrossthree-dimensionalcortical-subcorticalnetworksandthatcannotbecapturedwithinsingleunitrecordings(NicolelisandLebedev,2009). RehabilitativeBCIcanbedesignedeitherbyattachingneuralprosthesestotheimpairedbodypartsorbyre-stimulatingthedamagedsynapticnetworks;inanyofthecases,theideaistoexploitandpromoteneuroplasticity(Wangetal.,2010;Ramos-Murguialdayetal.,2013;Parketal.,2016;Darvishietal.,2017;Toriyamaetal.,2018;SongandKim,2019;Romero-Laisecaetal.,2020).Instrokepatientswithpareticmuscleswithoutresidualfingermovement,increasedelectromyographicactivitypostrehabilitationbyBCI-drivenorthosesexhibitsincreasedneuromuscularcoherencethatisessentialforrestoringmovementcontrol(Pfurtschelleretal.,2000;Ramos-Murguialdayetal.,2013).ExplicitapplicationoffunctionalelectricalstimulationregulatedbyEEG-basedmovement-relatedsignaturesfurthersuggestsaroleofBCIinrehabilitation(Zhaoetal.,2016).Increasedelectromyographicactivityinpareticmusclesisindicativeofplasticityinducedbyelectricalstimulation(DeMarchisetal.,2016).ForBCI-basedrehabilitationinareal-lifeenvironment,differentiatingbetweentask-inducedactivitiesandrestingstateactivitiesisakeyfactorforcontrollingtheprosthesisorstimulationmodality(Pahwaetal.,2015). Externallystimulatingtheaffectedbrainareasbyelectricormagneticfieldsholdspromiseforstrokerehabilitation.Arecentstudydemonstratedtheinductionofneuroplasticityinwhitematterandcorticalfunctionsinchronicstrokepatientsbymotorimagery-basedBCIandtranscranialdirectcurrentstimulationappliedtotargetedbrainareas(Hongetal.,2017).MagneticstimulationofbrainareasdrivenbyBCIincreasescorticalactivationinstrokepatients(Johnsonetal.,2018).Thelevelofneuroplasticityachievedpost-rehabilitationvariesacrosssubjectsand,thus,anindividual-specifictrainingsessionisnecessary(Leamyetal.,2014).TheuseofBCI-basedmotorrehabilitationforlocked-inpatientsislimitedbecausetheyareunabletofullyinteractwiththesystem(BirbaumerandCohen,2007).OtherexamplesofBCI-drivenrehabilitationsincludeoptimizingtheparametersfordeepbrainstimulationappliedintothesubthalamicnucleusinpatientswithParkinson'sdisease(Sandetal.,2017)andtreatingmajordepressivedisorderbyBCI-driventranscranialmagneticstimulation(Rayetal.,2015). Eitherbyprovidingdirectcontrolofassistivetechnologiesorbydirectneurostimulation,BCIcanhelppatientswhomaysufferfromamyotrophiclateralsclerosis,cerebralpalsy,brainstemstroke,spinalcordinjuries,musculardystrophies,orchronicperipheralneuropathies(Kauhanenetal.,2006;Iturrateetal.,2009;MakandWolpaw,2009;Allisonetal.,2010;Ramos-Murguialdayetal.,2013;Leamyetal.,2014;Botreletal.,2015;CombazandVanHulle,2015;Edelmanetal.,2015;Parketal.,2016;Zhaoetal.,2016;Alcaide-Aguirreetal.,2017;Geetal.,2017;Chiarellietal.,2018;Guyetal.,2018;Yuetal.,2018;RezazadehSereshkehetal.,2019;Jinetal.,2020;Zuoetal.,2020).Providingauxiliarydegreesoffreedomimprovesthequalityoflifeofpeoplewithdisabilitiessignificantly.Brainsignalscanbetranslatedtodrivewheelchairs(Galánetal.,2008;Iturrateetal.,2009;Perdikisetal.,2017;ToninandMillán,2020).IntegrationofBCIwithavision-guidedautonomoussystemwasshowntoeffectivelyperformthegraspingtaskusingaprostheticarminatetraplegicpatient(Downeyetal.,2016).Animplantedmicroelectrodearrayhasbeenproposedtooperateathree-dimensionalneuroprostheticdevice(Tayloretal.,2002). 3.2.SignalAcquisition,SignalProcessing,andModeling AsignificantnumberofstudiesarenowinvolvedincombiningmultimodalsignalacquisitionmodalitiestoaugmentcurrentBCIsystems.Forexample,simultaneousEEGandfMRIyieldcomplementaryfeaturesbyexploitinggoodtemporalresolutionofEEGandgoodspatialresolutionoffMRI(Debeneretal.,2006).EnhancedmulticlasssensorimotortasksclassificationperformanceusinghybridEEGandfNIRSsignalsimplicatestheimportanceoffeaturesextractedfrombothhemodynamicandelectricalactivities(Buccinoetal.,2016).MEGisanotherpotentialtooltocombinewithEEG,asitcapturesradially/tangentiallydipolesourcesincortical-subcorticalnetworksandaddscomplementaryinformationtoEEGsignals(Kauhanenetal.,2006).Skepticismmightstillpresentaboutthedetectionofbrainactivitiesoriginatedfromsubcorticalareas;however,anincreasingnumberofstudiesarguethatEEGandMEGcouldcapturesubcorticalactivities(Andersenetal.,2019;Minetal.,2020;Piastraetal.,2020).ArecenttrendistocombinedifferentsignalacquisitionmodalitiestogethertoimproveBCIefficiency.Table4highlightsmultimodalandhybridBCIapplications. TABLE4 Table4.Asummaryofmultimodalandhybridbraincomputerinterface(BCI)studies. Thecombinationofsignalprocessingandmachinelearningapproachesplayscriticalroleintranslatinganybrainsignaltoacommandforacomputerorotherexternaldevices.Tables2–5highlightdifferentsignalprocessingandmachinelearningtechniques.Representingsignalsinthetime-frequency-spaceisnecessarytoobtainphysiologicalcorrelatesofBCIoutcomes(McFarlandetal.,2006;Bashashatietal.,2007).Fouriertransform(FT)andautoregressivemodelsareexamplesoftimedomainrepresentationsofbrainsignalswhileshorttimeFTandwavelettransformareexamplesoftime-frequencyrepresentations(McFarlandetal.,2006;Bashashatietal.,2007).Incaseofspatialfiltering,themostpopularfilteringapproachesarecommonspatialpattern,independentcomponentanalysisandtheLaplacianfilter.Adiverserangeofinversemodelsallowtodiscerntheactualsourcesprojectedonthree-dimensionalcortical-subcorticalnetworks(Wronkiewiczetal.,2015;Sahaetal.,2019a).Extractedfeaturescanbetranslatedusingvariouslinearandnon-linearclassificationalgorithms.Examplesoflinearandnon-linearclassifiermodelsarelineardiscriminantanalysisandnon-linearkernel-basedsupportvectormachines(Lotteetal.,2007,2018). TABLE5 Table5.Asummaryofbraincomputerinterface(BCI)studiesinvolvinginvasiveprocedures. Sincethefirstpublicationin2000,commonspatialpatternisstilloneofthemostpopularmethodstorepresentmultichannelEEGsignalsbycorrespondingspatialcontents(Ramoseretal.,2000).Asadata-drivenmethod,itrequiresasignificantnumberoftrainingsamplestomodelthefilteringparameters.Incaseofsmalltrainingtrials,regularizingthecovarianceestimationworksbetterthanthetraditionalalgorithm(LotteandGuan,2010).OthermodificationsinspatialfilteringincludeprojectingEEGbyusingsparserepresentationandfilterbankspectraldivisionofrawsignals(Arvanehetal.,2014).Generally,spatialfilteringisapplicableinsubject-specificBCIdevelopmentalthoughrecentstudieshaveproposedestimatingthefiltercoefficientsfromasubjectandappliedthatfiltertoanothersubject,whichcontributednotrainingsample(Sahaetal.,2017,2018,2019a).Otherpopulardata-drivenmethodsincludelineardiscriminantanalysis,supportvectormachineandprincipalcomponentanalysis(Lotteetal.,2007,2018).Withtheexceptionaladvancementsincomputationalfacilitiesinthelastdecade,deeplearning-basedBCIparadigmsbyallowingtheevaluationoflargedatasetscouldsoonbecomeatrendinthecommunity(Chiarellietal.,2018;Kwonetal.,2019;NagelandSpüler,2019). Ontheotherhand,independentcomponentanalysisisablindsourceseparationmethodrequiringnotraining.Theestimationofindependentcomponentsisbasedonstatisticalpropertiesofthesignals(BellandSejnowski,1995).However,modelingtheactualcorticalsourcesasdipolesinthecomplexbrainanatomyfromthescalpEEGrecordingsseekstosolvetheso-calledinverseproblem(Qinetal.,2004;Kamousietal.,2005;Wronkiewiczetal.,2015;Sahaetal.,2019a).Morerecentsourcelocalizationmethodssuchaswavelet-basedmaximumentropyonthemeanrepresentEEG/MEGsignalsasrelevanttime-frequencycontentsandfinallytransformthemintospatialrepresentations(Linaetal.,2012;Sahaetal.,2019a).Notably,differentinversemethodsandtoolboxesdemonstrateconsiderablevariabilityinlocalizedsources(Mahjooryetal.,2017).Evenitisnotverystraightforwardtoknowtheexactsources,whicharetobemodeledusingEEG/MEG.Forexample,thegroundtruthdefinedbyimplantedelectrodesmightnotbe100%reliablebecauseofsparse(spatial)sampling.InthecaseoffMRI,themeasurementofneuralactivityisindirect.Notwithstanding,inversemethodshaveshownpromisefordesigningvariousBCImodels(Qinetal.,2004;Kamousietal.,2005;Wronkiewiczetal.,2015;Sahaetal.,2019a). 3.3.Neurosensors:The-State-of-the-Art Deeperregionsofthebrain,e.g.,subcorticalandcerebellarregions,contributetovariousneuronalactivities(Mülleretal.,2002;Wardmanetal.,2014).InterpretingthegenesisofcorticalsourcesfromcellulartoscalplevelsandRSNsspannedthroughoutthethree-dimensionalbrainspacecanguideBCIdevelopment(Donoghue,2008).Sensorswithcustomizeddesignaredevelopedtoadvancebrainsignalacquisitionmodalities.Neurosensorscanbeconstructedindifferentformslikeelectrical,optical,chemicalandbiological(DeisserothandSchnitzer,2013).DryEEGelectrodesareconvenient,butassumedtoprovidelowersignal-to-noiseratiocomparedtoconventionalwetelectrodes.Wetelectrodesmaycauseinconveniencetousersastheyuseconductivegelandrequireproperskinpreparationforminimizingtheskin-electrodeimpedance(Liaoetal.,2012).However,astudyondryelectrodes-basedBCIsuggestedthatdryelectrodecouldbeusedtocollectgoodqualitysignalsbydesigningthecircuitscarefully(Chietal.,2011).Furtherstudiessupportdryelectrodeswithwirelesssystemsthatcouldoffercomparablesignalqualityasofwetelectrodes,butwithmoreconvenience(DiFlumerietal.,2019;Marinietal.,2019;Hinrichsetal.,2020).Whileutilizingtheadvantagesofbothdryandwetelectrodes,quasi-dryelectrodesexploitingthemechanicalpropertiesofpolymercancapturesignalsascomparabletocommercialAg/AgClelectrodes(Motaetal.,2013).ToincreasethespatialresolutionofEEG,Petrovetal.haveproposedanultra-densesensorarrayof700–800electrodes(Petrovetal.,2014).Thesignal-to-noiseratiowastwiceashighasforhigh-densityEEGthathasupto256gold-coatedelectrodes.Anauricleelectrodewithstretchableconnectorwasproposedthatnotonlycanincreaseportabilitybutalsocanofferacomfortablealternativeforlongtermrecordings(Nortonetal.,2015).Theelectrodeisflexiblewiththealterationsofelectricalandmechanicalpropertiesofskin. Invasivesensorsmustbebiocompatible.Anovelorganicelectrochemicaltransistor-basedsensorenablestocollectneuralsignalsdirectlyfromthebrainsurface(Khodagholyetal.,2013).Thissensorisbiocompatibleandmechanicallyflexible,andthetransistor-baseddesignamplifiescapturedsignalslocally,thusprovidingmuchbettersignal-to-noiseratiothanconventionalECoG.Toenhancethesignalquality,carbonnanotubecoatingcandecreasetheelectrodeimpedanceand,thus,increasethechargetransfer(Keeferetal.,2008).Anotherinvasivebiocompatiblesensor,designedforrecordingpreviouslyinaccessiblespectraoflargeneuronpopulations,includesdatatransmissionforuseinnaturalenvironments(Yinetal.,2014).Withtheoutstandingprogressofnanotechnology,nanowireFieldEffectTransistorandotherp/njunctiondeviceshavepotentialforneuro-sensingmodalitiesforintracellularrecordings,eveninthedeepbrainregions(Kruskaletal.,2015).Oxleyetal.haveproposedstent-electrodearray(stentrode)thatinvolvesminimalinvasiveness(Oxleyetal.,2016,2017).Usingcomputer-guidedcatheterangiography,thestentrodecanbeplacedwithinarteriesorveinslocatedinsidethebrainanatomy.Capturinghigh-fidelitycorticalsignals,thistechnologywillsignificantlyreducetheriskfactorsofcraniotomy.Afollow-upstudyhasrecentlydemonstratedsuccessfulimplantationofthestrentrodeinhumansforlong-termneuralsignalrecording(Oxleyetal.,2020).Theinformationtransferrateforstrentrode-basedBCIwascomparabletothelandmarkstudybyVansteenseletal.withimplantedelectrodes(Vansteenseletal.,2016).AnotherimplantedECoGrecordercalledasWIMAGINE(WirelessImplantableMulti-channelAcquisitionsystemforGenericInterfacewithNeurons)allowswirelessneuraldataaccess(Mestaisetal.,2014).TheWIMAGINEhasrecentlybeentestedforlong-termreliabilityofdataacquisitionandanyriskassociatedwithcraniotomy(Kruskaletal.,2015;Sauter-Starceetal.,2019). Besideslarge-scalerecordingmodalitieslikeEEGandMEG,verysmall-scalerecordingsofneuronalactivitiesarecrucialforunderstandingbraincircuits'functionsandintra-andinter-neuroninteractions.Representationofanycognitivetaskasfunctionsofbothsmall-scaleandlarge-scaleneuronalinteractionsiscrucialfortheadvancementofneuroengineeringandBCI.Inthisregard,ahigh-densityneurosensorarraymadefromsiliconprobescombinedwithoptogeneticsenablessingleunitrecordings(Buzsákietal.,2015).Yangetal.proposedanovelmulti-planetwo-photonmicroscopethatcanbeusedtocapturemulti-layerneuronalstructureandmechanismwithcellularresolution(Yangetal.,2016).Otherpotentialimagingmethodsforinvestigatingcellsignalingincludecalciumimaging(GrienbergerandKonnerth,2012)andadvancedmicroscopewithchronicallyimplantedlenses(Resendezetal.,2016).Designerreceptorexclusivelyactivatedbydesignerdrugs,providesachemogenetictooltounderstandcell-signalingincludingelectricalactivitiesinmolecularlyclusteredcellgroups(SternsonandRoth,2014;Roth,2016).Anewultrasonic-basedwirelesssystem,calledneuraldust,enablestherecordingofelectromyogramandelectroneurogramonthemillimeterscale(Seoetal.,2016). 3.4.AffectiveComputing,Gaming,Robotics,andMiscellaneousApplications Futurecomputersareassumedtohaveemotionalandperceptualcapabilities,whichcouldextendtheusenotonlytoassistinghumansbutalsotomakingdecisions(Picard,2000).Computersmightabletorecognizeandinterpretunderlyingaffectivestatesbasedonphysiologicalandbehavioralvariables.RecentstudiesdemonstratedBCIisapotentialtooltoinvestigateaffectivestates,expandingtheapplicationsintopsychology(PihoandTjahjadi,2018;Songetal.,2018;Huangetal.,2019).Huangetal.haveproposedanEEG-basedBCItodetectpositiveandnegativeemotionsinducedbyvideostimulus(Huangetal.,2019). IntegrationofartsintoBCIisreferredtoasartisticBCI(Andujaretal.,2015).Inthelate1960s,DavidRosenboombeganexperimentingwithwaystolinkbrainfunctionswithmusicalproduction,perceptionofmusicalformsandmusicalproprioception(Rosenboom,2014).OtherexamplesofartisticBCIincludeaffectivestatesdetection,playingvideogamesandcontrollingvirtual/augmentedrealityenvironment.StudieshavedemonstratedthatausercanfullyoperatevideogamesbySSVEP-BCI(vanVlietetal.,2012;FilizandArslan,2020).Otherstudieshaveproposedhowmultipleuserscanparticipateinacollaborativegame,inwhichjointdecisionmakingisrequiredtocontrolthegamingenvironment(NijholtandPoel,2016;Sekhavat,2020).Anotherstudypreviouslysuggestedtheaggregationofinformationfromtwointelligenceanalysts'brainsignalsmayleadtobetterdecisionmakingthanone'sbrainsignals(Stoica,2012).Theunderlyingcausecouldbeexplainedbyinter-individualdifferencesinhumancognitiveandperceptualskills(Kleinschmidtetal.,2012).Collaborationbetweenusersmightassistanindividual'sdecisionmakingbydiversityinclusion.Amodifiedsetupcouldinvestigatehowpeopleinteractindifferentsocialcontexts,extendingBCIapplicationsinsociology(Amaraletal.,2017). Virtual/augmentedreality(V/AR)technologiestogetherwithBCIcouldofferimmersiveexperiencesandhavemanypotentialapplicationsincludingartsandneurofeedback(Andujaretal.,2015;Tremmeletal.,2019;Putzeetal.,2020).Brainpaintingallowsausertodrawlinesinavirtualcanvasbybrainsignals,whichgivesanalternativecommunicationchannelforpeoplewithpareticmotorfunctions(Botreletal.,2015).McClintonetal.havedevelopedabrainpaintingapplicationusingVRenvironment(McClintonetal.,2019).AnotherworkhasevincedVR-BCItomeasurecognitiveworkloadthatcancontributetoneuroergonomics(Tremmeletal.,2019).StudieshavealsousedimmersiveVRasabetterneurofeedbackoptionascomparedtothecomputerscreenleadingtoincreasedBCIaccuracy(Luuetal.,2016;Školaetal.,2019;Vourvopoulosetal.,2019;Julianoetal.,2020).Vourvopoulosetal.haveintegratedtheprinciplesofVRandBCIintoaplatformcalledREINVENTformotorrehabilitation(Vourvopoulosetal.,2019).Likewise,BCIwithARcanbeusedtoremotelycontrolarobotforrehabilitatingchildrenwithattention-deficit/hyperactivitydisorder(Arpaiaetal.,2020). WhileBCI-drivenroboticcontrollerscanofferadvancedassistivetechnologyforpeoplewithmobilityconstraint,itmayalsoaugmenthumanergonomicperformanceforhealthysubjects(Millanetal.,2004;Gandhietal.,2014;Tidonietal.,2016;Perdikisetal.,2017;Spataroetal.,2017;YuanandLi,2018;Dengetal.,2019;Toninetal.,2019;ToninandMillán,2020).EEG-basedBCI-drivencontrollerofmobilerobotorwheelchairhasdemonstratedthepossibilityofthistechnologyinroboticsindustry(Millanetal.,2004;Tidonietal.,2016;Perdikisetal.,2017;YuanandLi,2018;Dengetal.,2019;Toninetal.,2019).BCIcanalsobeusedforcontrollinghumanoidrobotsremotelyusingEEG(Spataroetal.,2017),suitableinhazardousenvironments,forexamplebysendingarobotinacoalmineforexecutingataskthatispotentiallyunsafeforahuman.Inspace,BCIcanbeusedtomonitorastronauts'workingcapacityandtodriveanexoskeleton(Menonetal.,2009;deNegueruelaetal.,2011).Intheabsenceofgravity,workingbecomestediousandinconvenient.Furthermore,astronauts'workingtimeisprecious.BCI-drivensystemscouldbepracticalforimprovingastronauts'functionality,efficiencyandsafety(Summereretal.,2009;Farwelletal.,2014;Botreletal.,2015;Ortizetal.,2016;Wangetal.,2016a;Vourvopoulosetal.,2019;Singhetal.,2020). Recently,brain-to-braininterface(BBI)experimentsthatinvolvedecodingsender'scognitiveintentions,translatethemintocommandsforstimulatingreceiver'sbrain,havebeenexplored(Pais-Vieiraetal.,2013;Raoetal.,2014;Jiangetal.,2019).In2013,researchersimplementedadirectBBIsysteminwhichoneratwasabletosharesensorimotorinformationtoanotherrat(Pais-Vieiraetal.,2013).Intracorticalmicrostimulationwasusedtostimulatethereceiver'stargetbrainareas.Anearlyattempttodevelopsensorimotorrhythm-basedBBIbetweentwohumansubjectsusednon-invasiveEEGandtranscranialmagneticstimulationhasbeenproposedbyRaoetal.(2014).Othertotalnon-invasiveBBIexperimentshaveproposedsharingpseudo-randombinarystreamsencodedwordsbetweenhumansubjects(Grauetal.,2014)andplayingcollaborativegames(Stoccoetal.,2015).Figure2illustratesatimelineforcurrentadvancesofBCIindiverseapplications. FIGURE2 Figure2.Aschematicillustrationoftheevolutionofthebraincomputerinterface(BCI)applications:Cognitive&PerceptualLearning/Rehabilitation(McMillanetal.,1995);OrthosisControl(Pfurtschelleretal.,2000);MusicBCI(Rosenboom,2014);Robotics(Millanetal.,2004);WheelchairControl(Iturrateetal.,2009);DrowsinessDetection(Linetal.,2008);AffectiveComputing(Zanderetal.,2009);BrainRacers(Perdikisetal.,2017);MultiplayerGaming(NijholtandPoel,2016);Brain-to-BrainInterface(Raoetal.,2014). 4.EthicalConcernsandSocioeconomicContexts IrrespectiveofthescientificbreakthroughsinBCIfield,therearekeyfactorspertainingtosafety,ethics,privacyprotectionanddataconfidentiality,communityacceptanceandsocioeconomicaspectsthatshouldbeconsideredwithadequateprecautionstomaximizeusers'benefitsandsocialimpacts(IllesandBird,2006;BostromandSandberg,2009;Jebari,2013;McCullaghetal.,2014).ObtaininganethicallysoundinformedconsentfromaBCIwornpatientmaybechallengingforBCIresearchersduetodifficultyincommunicatingandthelackofalternatives.However,moreawarenessandattentiontoethicspoliciesarerecommendedtoimprovethechanceforpatientstogetadequateinformation. PhysicalandmentalsafetyofBCIusersisimportant.Invasiveproceduressuchasdeepbrainstimulationandintracorticalmicroelectrodearraymaycausepostoperativepsychologicalandneurologicalsideeffects(JotterandandGiordano,2011;Gilbert,2015;Maslenetal.,2015).Additionally,bleedingandinfectionsareinfrequentbutdooccurandmayrequireremovalorfurthermaintenanceoftheimplantedelectrodes.Guidelinesarerequiredtosafelyadvanceneurotechnologies(GoeringandYuste,2016),becauseBCIdevicescanalterbehaviorand,thus,introducepotentialthreatstoone'semotions,personalityandmemories;moregenerallyone'smind.Forhumanbrain-to-braininterfaceapplications(Raoetal.,2014;Stoccoetal.,2015),onemaydefineanupperboundforresearchdepthskeepinginmindthenecessityofethicalutilizationofthistechnology.Becausebothsenderandreceiverplaycomplicatedroles,morespecifically,sender'sintentionalmanipulativecontroloverneuralsignalsmightaltertheanticipatedoutcome.Alteringhumancognitiveandpossiblymoralcapacityraiseaseriousethicalquestionanditisnotpredictableifthecognitivechangesreversibleandefficacious(Nakazawaetal.,2016). Auser'sexpectationsofachievingextendedorauxiliarydegreeoffreedommaynotbefulfilled,andeventheunfamiliarriskfactorscandiminishtheaccomplishedadvantageofusingBCI(Clausen,2011;Schicktanzetal.,2015).CreatingbroadawarenessofBCItechnologyanditsprosandconswouldeducatepeople,whofearunnecessarytechnologicaldependency(Hobsonetal.,2017).However,successfulclinicaltrialsofsophisticateddevicessuchasstrentrodeorWIMAGINEareessentialtodemonstratepotentialadvantages,especiallyforpeoplesufferingfromanyformofcognitivedisability(Sauter-Starceetal.,2019;Oxleyetal.,2020).Inthecaseofhealthyusers,itshouldnotbetoodifficulttocreateacceptancetoabroadercommunitywhendryelectrodescouldofferthelong-termoperationofaBCIapplicationwithlittlemaintenanceeffort(DiFlumerietal.,2019;Marinietal.,2019;Hinrichsetal.,2020). ItiscriticaltointroduceasuitableactforlawfulutilizationofBCIandpreservationofprivacyandconfidentialityofstoreddata.Recentstudieshavedemonstrateddecodingofpasswordorrecognizingfacesutilizingconsumer-gradeBCIsuccessfully,promptingapotentialconcernofanyillegitimateaccesstousers'rawdataandtheirfurtherexploitation(Martinetal.,2016;Alomarietal.,2019).Forexample,affectivestatesdefineusers'moraljudgmentandemotionaltraits.Thus,itiscriticaltolimittheapplicationsofaffectiveBCIwhilepreservingsensitiveinformation(SteinertandFriedrich,2020).Necessaryprecursorinitiativesshouldproposeapplication-specificBCIframeworks,whichcanrestrictunauthorizedaccesstostoreddataorthesystem(IencaandHaselager,2016).Forexample,illicitaccesstoawirelessBCI-drivenlimbandmanipulativereprogrammingofacomputer-guidedneuro-stimulationhavedemonstratedtheimportanceofestablishingresilientsafeguardstoBCIuse(Denningetal.,2009).Agarwaletal.haveproposedcryptographicprotocolsasintegratedpartsofBCItopreservetheprivacyofauserbykeepingconfidentialinformationobscuretoothers(Agarwaletal.,2019).Withoutevaluatingsocioeconomic,ethicalandpolicyissues,thecommercializationofBCIwouldhindertheprogressinthisfield(EatonandIlles,2007). BycreatingacommonnetworkingplatformforBCIresearchersworldwide,theimmediatepropositionofacomprehensivelistofuniversalguidelinesiskeytosustainableadvancementsofthefield(VaadiaandBirbaumer,2009).Variousalliance-basedprojectsarerunningascommonplatformsforadvancingtheknowledgeofneuroscience,forexample,bystrengtheningeffortstofundneuroscienceresearchprojects(Grillneretal.,2016).TheEuropeanUnionalongwithitspartneruniversitieshaveinitiatedtheHumanBrainProject.Inaddition,theBrainInitiativehasbeenannouncedbytheWhiteHouse.Inouropinion,advancedunderstandingofbasicneuroscientificphenomenawilldeterminethestructure,efficacyandapplicationsoffuturisticBCI. 5.Conclusion NumerousgroundbreakingadvancesinneurosensorsandcomputationaltoolsheraldgreatpromiseformoresophisticatedanduserfriendlyBCIsystemsrequiringnoorlittlemaintenance.Inadditiontohi-fidelitysignalacquisition,significantprogressinsignalprocessingandmachinelearningtools,theircomplementaryroles,andhighcomputationpowerandincreasedmobilityofcomputershavesignificantlycontributedintheemergenceofBCItechnologies.ThefutureofBCItechnologywillrelygreatlyonaddressingthefollowingkeyaspects: •ElucidatingtheunderlyingpsychophysiologicalandneurologicalfactorsthatpotentiallyinfluenceBCIperformance. •Designinglessinvasivesensorswithreliablesignalacquisitionandresolution,whileconsideringportability,easymaintenance,andaffordability. •Modelingsession-to-sessionandsubject-to-subjectinformationtransferforthepropositionofmoregeneralizedBCImodelswithinsignificantornocalibrationrequirement. •Establishingbroadconsensusonethicalissuesandbeneficialsocioeconomicapplicationofthistechnology. AuthorContributions SSconceivedtheinitialidea,wrotethefirstdraft,andgeneratedallfiguresandtables.KM,KA,RM,GN,andSDparticipatedinthediscussionandcommentedonthedraft.AHKandMBprovidedfurtherinsightandhelpedSStofinalizethestructureandmaterials.Allauthorsreadandapprovedthefinalpaper. ConflictofInterest Theauthorsdeclarethattheresearchwasconductedintheabsenceofanycommercialorfinancialrelationshipsthatcouldbeconstruedasapotentialconflictofinterest. Acknowledgments Thismanuscripthasbeenreleasedasapre-printathttps://arxiv.org/(Sahaetal.,2019b).AuthorswouldliketothankProf.MoritzGrosse-Wentrupforprovidinghisvaluablefeedback.Thisworkwaspartiallysupportedbyagrant(AwardNo.RC2-2018-022(HEIC)andKKJRC-2019-Health2)fromKhalifaUniversity,AbuDhabi,UAE. 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Keywords:braincomputerinterface,hybrid/multimodalBCI,neuroimagingtechniques,neurosensors,electrical/hemodynamicbrainsignals,cognitiverehabilitation Citation:SahaS,MamunKA,AhmedK,MostafaR,NaikGR,DarvishiS,KhandokerAHandBaumertM(2021)ProgressinBrainComputerInterface:ChallengesandOpportunities.Front.Syst.Neurosci.15:578875.doi:10.3389/fnsys.2021.578875 Received:01July2020;Accepted:06January2021;Published:25February2021. Editedby:AlessandroE.P.Villa,Neuro-HeuristicResearchGroup(NHRG),Switzerland Reviewedby:DongruiWu,HuazhongUniversityofScienceandTechnology,ChinaDavidR.Painter,TheUniversityofQueensland,Australia Copyright©2021Saha,Mamun,Ahmed,Mostafa,Naik,Darvishi,KhandokerandBaumert.Thisisanopen-accessarticledistributedunderthetermsoftheCreativeCommonsAttributionLicense(CCBY).Theuse,distributionorreproductioninotherforumsispermitted,providedtheoriginalauthor(s)andthecopyrightowner(s)arecreditedandthattheoriginalpublicationinthisjournaliscited,inaccordancewithacceptedacademicpractice.Nouse,distributionorreproductionispermittedwhichdoesnotcomplywiththeseterms. *Correspondence:SimantoSaha,[email protected];MathiasBaumert,[email protected] COMMENTARY ORIGINALARTICLE Peoplealsolookedat SuggestaResearchTopic>