Brain-Machine Interface (BMI) in paralysis

Update

article

Brain-Machine

Interface

(BMI)

in

paralysis

U.

Chaudhary

,

N.

Birbaumer

,

M.R.

Curado

*

a_{InstituteofMedicalPsychologyandBehavioralNeurobiology,UniversityofTu¨bingen,Germany} b_{OspedaleSanCamillo,IRCCS,Venezia,Italy}

c_{DepartmentofNeurology,Albert-Ludwigs-UniversityFreiburg,Freiburg,Germany}

1. Introduction

A brain-machine interface (BMI) uses brain activity directly withoutanymotorinvolvementfor activationofa computeror other external devices. A considerable amount of scientific literaturewascreatedonBMIsduringthepast15yearsbutmost ofthisliteratureisexperimentalinnature,controlledstudieson clinicalapplicationsarerare.Herewepresentanoverviewofthe available studies, which fulfill at least some methodological criteriaofacontrolledclinicaltrial.Wefocusontwoapplications where most of the work was done: BMI in paralysis from

amyotrophic lateral sclerosis (ALS) and BMI in the motor rehabilitationofchronicstroke.

2. ALSandneedforBMI

Amyotrophiclateralsclerosisisaprogressivemotordiseaseof unknownetiologyresultingeventuallyinacompletedestruction of the peripheral and central motor system but only affecting sensoryorcognitivefunctionstoaminordegree[1].Thereisno treatment available;patients have todecide toacceptartificial respirationandfeedingafterthediseasedestroysrespiratoryand bulbarfunctionsfortherestoftheirlifeortodieofrespiratoryor related problems. If they opt for life and accept artificial respiration,thediseaseprogressesuntilthepatientlosescontrol ofthelastmuscularresponse,whichisusuallytheeyemuscleor

ARTICLE INFO

Articlehistory:

Received10November2014 Accepted10November2014 Keywords:

Brainmachineinterface BMI

Braincomputerinterface BCI

Stroke

Amyotrophiclateralsclerosis ALS

ABSTRACT

Introduction:Brain-machineinterfaces(BMIs)usebrainactivitytocontrolexternaldevices,facilitating

paralyzedpatientstointeractwiththeenvironment.Inthisreview,wefocusonthecurrentadvancesof

non-invasiveBMIsforcommunicationin patientswithamyotrophiclateralsclerosis(ALS) andfor

restorationofmotorimpairmentafterseverestroke.

BMIforALSpatients:BMIrepresentsapromisingstrategytoestablishcommunicationwithparalyzedALS

patientsasitdoesnotneedmuscleengagementforitsuse.Distincttechniqueshavebeenexploredto

assessbrainneurophysiologytocontrolBMIforpatients’communication,especially

electroencephalo-graphy(EEG)and morerecentlynear-infrared spectroscopy(NIRS). Previousstudiesdemonstrated

successfulcommunicationwithALSpatientsusingEEG-BMIwhenpatientsstillshowedresidualeye

control,butpatientswithcompleteparalysiswereunabletocommunicatewiththissystem.Werecently

introducedfunctionalNIRS(fNIRS)-BMIforcommunicationinALSpatientsinthecompletelocked-in

syndrome (i.e., when ALS patients are unable to engage any muscle), opening new doors for

communicationinALSpatientsaftercompleteparalysis.

BMIforstrokemotorrecovery:Inadditiontoassistedcommunication,BMIis alsobeingextensively

studiedformotorrecoveryafterstroke.BMIforstrokemotorrecoveryincludesintensiveBMItraining

linkingbrainactivityrelatedtopatient’sintentiontomovethepareticlimbwiththecontingentsensory

feedbackofthepareticlimbmovementguidedbyassistivedevices.BMIstudiesinthisareaaremainly

focusedonEEG-ormagnetoencephalography(MEG)-BMIsystemsduetotheirhightemporalresolution,

whichfacilitatesonlinecontingencybetweenintentiontomoveandsensoryfeedbackoftheintended

movement.EEG-BMItrainingwasrecentlydemonstratedinacontrolledstudytosignificantlyimprove

motorperformanceinstrokepatientswithsevereparesis.NeuralbasisforBMI-inducedrestorationof

motorfunctionandperspectivesforfutureBMIresearchforstrokemotorrecoveryarediscussed.

ß2015ElsevierMassonSAS.Allrightsreserved.

* Correspondingauthorat:DepartmentofNeurology,Albert-Ludwigs-University Freiburg,BreisacherStr.64,79106Freiburg,Germany.Tel.:+49076127053310.

E-mailaddress:[email protected](M.R.Curado).

Available

online

at

ScienceDirect

www.sciencedirect.com

http://dx.doi.org/10.1016/j.rehab.2014.11.002

theexternalsphincter.Theresultingconditioniscalledcompletely locked-instate(CLIS) [2].Ifrudimentary controlofatleastone muscleispresent,wespeakofalocked-instate(LIS)[2].Almostall peoplewithALSexperienceamotorspeechdisorderasthedisease progresses.Initialsymptomstypicallydonotinterferewithspeech intelligibilityandmaybelimitedtoareductioninspeakingrate,a changeinphonatory(voice)quality,orimprecisearticulation.At somepointinthediseaseprogression,80to95%ofpatientswith ALSareunabletomeet their dailycommunicationneeds using naturalspeech.Later,mostbecomeunabletospeakatall[3].For them,communicationsupportinvolvesa rangeofaugmentative and alternative communication(AAC) strategies involving low-andhightechnology(speechgeneratingdevices)options[4]. Clin-icaldecision-makingrelatedtocommunicationisquitecomplexas screening, referral, assessment, acquisition of technology, and trainingmustoccurinatimelymanner,sowhenresidualspeechis no longer effective, AAC strategies are in place to support communication related to personal care, medical care, social interaction,community involvement,and perhapsemployment. Hencethereisaneedforanassistivetechnologytohelppatientsin CLIStocommunicateneedsandfeelingstotheirfamilymember/ caregiver.

3. TypesofBMIs

Brain-machine interface technology hasgenerated consider-ableresearchinterestforthe‘‘locked-in’’patientssuchasthosein thelatestagesofALS.BMIresearchincludesinvasive(implantable electrodes on or in the neocortex) and non-invasive means (includingelectroencephalography(EEG), magnetoencephalogra-phy(MEG), functional magneticresonance imaging (fMRI), and near-infrared spectroscopy (NIRS)) to record brain activity for conveying the user’s intent to devices such as simple word-processingprograms.Non-invasive methods havebeen utilized more extensively than invasive methods for people with dis-abilities(suchasthosewithALS)[5–7].WhilethosewithALSand otherconditions whoareina ‘‘locked-in’’statehave motivated researchinthisarea,veryfewsystemshavebeensuccessfulwith thispopulation.

3.1. EEG-basedBMIforALSpatients

Three differenttypes ofEEG-based BMIare currentlyin use namelyslowcorticalpotential(SCP)-BMI, sensorimotorrhythm (SMR)-BMIandP300-BMI.Based onthedetailed comparisonof three different signatures of EEG-based BMIs as reported by Birbaumer [2], it was concluded that in ALS patients with functioningvisionandeyecontrol,SMR-BMIandP300-BMIshows the most promising results. SCP-BMIs need more extensive trainingthanotherBMIsbutmayhavethebeststabilityandare moreindependent of sensory,motor,and cognitivefunctioning necessaryforitsapplicationintheLISandtheCLISpatients.The patientsdescribedearlier[8]hadhighsuccessrateswithSCP-BMI trainingbutonlyaftermanysessions.Ithasbeenpostulatedthat somecognitiveimpairmentandchangesinEEGsignaturesinlate stageALSmaycontributetothelackofsuccessusingEEG-BMI technologyasthetechnologywasintroducedaftertheparticipants hadbecome‘‘locked-in’’[5,9].KueblerandBirbaumer[10]have shownthatpatientsinCLISdonotreachsufficientBMIcontrolfor communicationwithEEGparameters.KueblerandBirbaumer[10]

speculatedthatextinctionofgoaldirectedthinkingmayprohibit operantlearning of brain communication. The most successful application for communication has occurred in people at the beginningstagesofthedisease[11–13].Hencethereisaneedto find an alternative neuroimaging technique to design a more effectiveBMItohelpALSpatientinCLISwithcommunication.

3.2. fMRI-basedBMIforALSpatients

fMRImeasuresincreasesanddecreasesofparamagneticloadof blood flow toactivated poolsof neurons, particularlyto apical dendrites [14]. Paramagnetic charge is determined by blood oxygenation level dependent (BOLD) flow, which reflects local metabolicdeficienciesofthevascularbedsupplyingtheneurons. Logothetisetal.[14]haveshownthatthecorrelationoflocalblood flow change and the BOLD signal is particularly high for the neuronal inflow to the apical dendrites reflecting primarily intracorticalactivity.ThefMRI-basedBMIisdifficulttoapplyon ALSpatientbecauseitisexpensive,bulkyandimpossibletomove topatients’home.Moreover,thepatientenclosedinthescanner doesnotexperienceasatisfyingenvironmentforcommunication. Still instrumental learning of BOLD control turned out to be successfulinneuropsychiatricdisorders[15].

3.3. fNIRS-basedBMIforALSpatients

NIRS is anemerging neuroimagingmodality which employs near-infrared light to non-invasively or invasively investigate cerebral oxygenation changes in healthy and neurologically challenged adults and children [16]. It has reasonable spatial (about1cm)and goodtemporal (about1ms)resolution andis relatively robust to motion artifact, thereby enabling it to be suitableforinvestigatingeverydaytasks[16].Thusincontrastto functional magnetic resonance imaging, a NIRS-based BMI can easily be applied at the bedside of these highly impaired and difficulttomovepatientsindesperateneedforcommunication.

Sitarametal.publishedthefirstcontrolledevaluationofaNIRS BMI.Usingmotorimagerywitha20channelsNIRSsystemover sensorimotor cortex they reported 89% correct classification of rightandlefthandimagerywithoutanytrainingandtheuseofa hiddenMarkovmodelasaclassifier[17].VeryrecentlyNIRSwas successfullyusedtoinvestigatethefunctionalactivationsinthe cortexofaCLISpatientinresponsetoauditorilypresentedstimuli containing correct or incorrect statements and open questions

[18].Thehemodynamicchangeinthemotorcortexof theCLIS patientwasrecordedacrossmanysessionsspreadovermorethan ayearandwasusedtotrainaclassifiertopredictthe‘‘yes’’and ‘‘no’’answeringpatternof theCLIS patientwhowaspreviously trained to use an EEG-BMI without success [19]. The trained classifierwasabletoprovideonlinefeedback(‘‘youranswerwas classifiedas(in)correct’’)tothepatientwithperformancerateof 71.76%. Thisis thefirst carefully documented case of commu-nicationinaCLISpatientwithBMI,whichholdspromiseandraises thehopeforcommunicationinCLIS.Hence,tofurthervalidatethe preliminaryfindingsofourlabandrefinethetechnologyof fNIRS-basedBMIforcommunicationinCLISpatientsextensivestudies arepresentlycarriedoutonCLISpatientsusingcombined fNIRS-EEG-basedBMIs.Thecompletesetupofthecombined fNIRS-EEG-basedBMIsdevelopedforcommunicationinCLISpatientisshown inFig.1.

4. BMIformotorrecoveryinchronicstrokepatients

Strokeisoneoftheleadingcausesofacquireddisabilityinthe adult population worldwide [20]. While for patients with incomplete hand paralysis repetitive motor tasks may restore motorfunction[21,22],patientswithseverehandparesisdonot profitfromcurrentrehabilitationstrategiesastheyarenotableto perform the therapeutic movements. For those patients, BMI trainingrepresentsapromisingstrategytorecovermotorfunction. BMI training for stroke motor recovery involves repetitive motor tasks with the paretic limb through decoding of brain signals related toprocessing of motor information (e.g., actual

movementormotorimagery) and contingentmovementof the pareticlimbguidedbyanexternaldeviceprovidingsensory(visual andkinesthetic)feedback.Thebrainsignalmostcommonlyusedto controlBMI formotor recoveryis thesensorimotor rhythm,or SMR,anoscillatorybrainactivitylocatedover thesensorimotor cortex in the range of 8–13Hz [23]. The SMR decreases its amplitude(SMR desynchronization)during processingofmotor informationwhileahighSMRamplitude(SMRsynchronization)is associated with processing of rest or an ‘‘idling cortical area’’

[23].Therefore,changesinSMRamplitudecanbeusedtotriggeran external deviceguiding pareticlimb movements and providing sensoryfeedbackcontingenttouser’sintentiontomove(Fig.2). The first demonstration of a SMR-based BMI for control of paralyzed limb movements driven by an external device was givenin2000byPfurtscheller’sgroup,inastudywithatetraplegic patient[24].

4.1. RecentdevelopmentsofBMIinstrokemotorrecovery

In2008itwasdemonstratedthatchronicstrokepatientswith affectedsubcorticalorcorticalsitescouldsuccessfullycontrola magnetoencephalography(MEG)-BMIbyvoluntarilymodulating ipsilesional SMR amplitude while receiving contingentsensory feedbackoffingersextensiondrivenbyahandorthosis[25].The choiceofipsilesionalSMRtocontroltheBMIwasbasedonfindings indicating that motor recovery after stroke relies mainly on functionalreorganizationofipsilesionalmotorcortex[26]. Follow-ingthis study,a seriesofsingle-case studiesbydistinctgroups worldwidesuggestedthatEEG-BMItrainingbasedonmodulation ofipsilesional SMRactivitypromote motor recoveryin chronic stroke patients with severe hand paresis after few weeks of intervention[27–30].Whilesomegroupsusedahandorthosisto

guidepareticlimbmovements[28,29],othergroupsdemonstrated thatfunctionalelectricalstimulation(FES)—i.e.,electricalpulses deliveredtothepareticmuscles,promotingmusclecontraction— isafeasiblealternativetodriveparetichandmovementsinBMI trainings[27,30].

In2013,wedemonstratedinacontrolleddouble-blindstudy efficacy of BMI training to promote motor recovery in chronic stroke patients with severe hand paresis after four weeks of intervention[31].Twogroupsofpatientsweretested:whileinthe experimentalgroupipsilesionalSMRdesynchronizationcontrolled contingent sensory feedback of the paretic upper limb via movementsofamechanicalorthosis,intheshamgrouporthosis movementwasrandom,i.e.,unrelatedtoSMRdesynchronization. After intervention, the experimental group only significantly improved upper limb motor functionand presentedsignificant lateralizationofbrainactivitytowardstheipsilesionalhemisphere whileperformingmovementswiththepareticlimb[31].Inthe neurophysiological aspect, this study provides evidence that contingency between intention to move the paretic limb and the sensory feedback associated to its movement promotes ipsilesionalbrain reorganizationandplays a keyrole on motor recovery promoted by BMI trainings. Clinically, these findings strengthen the relevance of BMI trainings to recover motor functioninchronicstrokepatientswithseverehandparesis,i.e., noteligibleforcurrentrehabilitationstrategies.

Whilethesestudiesusedelectrophysiologicalbrainsignalsto controlaBMI,metabolicsignalsderivedfrombloodoxygenation frommotor-relatedbrainareasarefeasiblealternativestocontrol non-invasive BMI systems for stroke motor rehabilitation. For instance,Sitaram’sgroupin2007[17]andGassert’sgroupin2013

[32]demonstratedthatchangesincerebralbloodflowandlocal oxygen consumption measured with NIRS can be used to Fig.1.ExperimentalsetupoffNIRS-EEG-basedBMIsystemdevelopedforcommunicationinCLISpatientsbyresearchersfromtheInstituteofMedicalPsychologyand BehavioralNeurobiology,UniversityofTu¨bingen.ThefNIRS-EEG-basedBMIconsistsoffNIRSsystem,EEGsystem,computersandanaudiosystem.ThefNIRSsystemconsists of8sourcesand8detectorswhichmeasurethechangeincerebralhemodynamicsinresponsetoauditorilypresentedstimulitoCLISpatient.Thehemodynamicchange recordedfromthemotorcortexisusedtotrainaclassifiertopredictthe‘‘yes’’and‘‘no’’answeringpatternoftheCLISpatient.TheEEGsystemrecordstheelectricalactivityin responsetoauditorilypresentedstimulitoCLISpatient,whichisthenusedtotrainaclassifiertopredictthe‘‘yes’’and‘‘no’’answeringpatternoftheCLISpatient.Thetrained fNIRSandEEGsignalclassifieristhencombinedtoprovideneurofeedbacktotheCLISpatient,whentheyattendtotheauditorystimuli,as‘‘youranswerwasclassifiedas(in) correct’’.

successfullydecodemotorimageryinthemotorcortex.DeCharms andcolleaguesand Hallett’sgroupdemonstratedthat real-time functionalmagneticresonanceimaging(rt-fMRI)canbeusedto providecontingentvisualfeedbackofmotorimagery[33,34]. How-ever,the efficacy of BMI-based on hemodynamic responses to promotemotor recoveryin patients withchronicstrokeis still unexplored. Furthermore, other issues may complicate clinical implementationofthesetechniques,e.g.,hemodynamicresponses areslowercomparedtoelectrophysiologicalsignals(whichmay affect contingency between intention to move and sensory feedback), and rt-fMRIis technically demandingand expensive

[17].Moreover,forrt-fMRItheneedofheadfixationinparallelto the need to perform motor tasks with assistance of non-ferromagneticorthoticdevicesinsidethescannermakesitavery challengingtechniqueforthedevelopmentofmotorrehabilitation strategiesafterstroke.

5. NeuralbasisofstrokemotorrecoveryafterBMItraining ItisassumedthatmotorrecoverypromotedbyBMItraining followsprinciplesof skill learning and Hebbian plasticity. Skill learning involves a sensory stimulus that influences response planning,the actualresponsetothestimulus and acontingent feedback (reward or punishment) upon the response. During training,responseplanningconstantlyadaptsitselftodecreasethe difference between the anticipated feedback and the actual feedbackupontheresponse[35].Accordingly,inhealthysubjects SMR-BMI training with contingent feedback improves BMI performanceandmotorlearning,enhancingSMR desynchroniza-tionduringmotorimagery[36].Itisplausiblethatcorticalplasticity mechanisms underlying motor skill learning [37,38] facilitates cortical reorganization in preserved ipsilesional motor-related

brainareasduringBMItrainingwithcontingentsensoryfeedback, promotingmotorrecoveryinstrokepatients.Accordingly,afterBMI intervention linking ipsilesional SMR desynchronization (asso-ciatedwithpatient’sintentiontomove)withassistedmovementof the paretic limb, stroke patients learned to control SMR-BMI; showed significant lateralization of brain activity towards the ipsilesional hemisphere when moving the paretic limb; and improved motor performance [31]. This findingis in line with previousresultsindicatingthatmotorrecoveryafterstrokerelies predominantly on functional reorganization of the ipsilesional motorcortex[26].

6. FutureresearchinBMIforstrokemotorrecovery

SeveralgroupsareengagedinthedevelopmentofBMI-based approaches for strokemotor recovery. Gassert’s group demon-stratedthatcombiningbrainhemodynamicresponseswithbody physiological signals (hybrid Brain-Body-Machine Interface, or BBMI)improvesreliabilitytodetecthealthyparticipants’intention to move as compared to brain hemodynamic responses alone

[32].Thisapproachmayimprovecontingencyofsensoryfeedback and facilitate participant’s control of limb movement in NIRS-basedBBMIsystems.However,studiesevaluatingwhether NIRS-based BBMI systems also increase reliability to detect stroke patients’intentiontomovearestill missing,andtheefficacyof NIRS-based (B)BMI torestore motor function in chronic stroke patientsisstillunexplored.Inaddition,reliablecontrolofhybrid BBMI systems combining EEG signals with muscular activity (electromyography,orEMG)weredemonstratedbyMilla´n’sgroup

[39],butdirectcomparisonbetweenreliabilityofusers’controlin EEG-basedBMIandEEG-EMG-basedBBMIsystemsstillneedstobe tested. If hybrid EEG-EMG-based BBMI systems have superior Fig.2.OverviewofEEG-BMIsystemformotorrehabilitationafterstroke,fromtheInstituteofMedicalPsychologyandBehavioralNeurobiology,UniversityofTu¨bingen.The EEG-BMIsystemiscomposedby16-channelsEEG,acomputerandamechanicalhandorthosisattachedtotheupperlimbtoextendandclosethefingers.BMItraining sessionsarecomposedbyseveraltrials,eachtrialcontaininganactivitywindowandarestwindow.Auditorycuesinformtheuserwheneachactivityandrestwindowstarts. Duringrestwindow,usersareinstructedtonotmoveandtonotimaginemovements.Duringactivitywindow,usersareinstructedtomovethelimbattachedtothehand orthosis.Brainactivityisregisteredduringthewholetrainingsession.Signalprocessingstepsextractsensorimotorrhythm(SMR)duringrestandactivitywindows.During activitywindowSMRamplitudeisclassifiedineitherSMRdesynchronization,i.e.,SMRamplitudeisdecreasedascomparedtorestSMRamplitude;orSMRsynchronization, i.e.SMRamplitudeisnotsignificantlydecreasedascomparedtorestSMRamplitude.SMRdesynchronizationtriggersorthosismovement,guidingfingersextension.During SMRsynchronizationtheorthosisdoesnotmove.FormoreinformationaboutTu¨bingenEEG-BMIsystemforstrokemotorrecovery,pleasesee[31].

reliabilityofuser’scontrolascomparedtoEEG-basedBMIsystems, itisplausiblethattheymayimproveBMItrainingeffectsonmotor recoveryafterstroke.

Besides development of hybrid BBMIs, brain stimulation techniquesalsorepresentfeasiblecomplementaryapproachesto primeBMIinterventioneffects.Brainstimulation,e.g.,transcranial directcurrentstimulation(tDCS),canmodulatebrainexcitability andwasdemonstratedtofacilitatemotorskilllearninginhealthy participants [40–42] and stroke patients [43,44], and improve motor function in chronic stroke patients [45]. Recent studies suggestthatexcitatorytDCSappliedintheipsilesionalhemisphere canbeusedtoincreasestrokepatients’SMRdesynchronization, whichsupportstheuseofbrainstimulationasaconditioningtool to improve patients’ BMI control [46,47]. However, these preliminary findings were based on few patients and further studiesbasedoncontrolled designarenecessarytovalidatethe impactofcombiningbrainstimulationandBMIsystemsformotor recoveryafterstroke.

Disclosureofinterest

The authors declare that they have no conflicts of interest concerningthisarticle.

Acknowledgements

This work was supported by the Deutsche Forschungsge-meinschaft(DFG,Koselleck),StiftungVolkswagenwerk(VW),and Baden-Wu¨rttemberg-Stiftung. M.R. Curado was supported by CoordinationfortheImprovementofHigherEducationPersonnel (CAPES, Brazil) and Deutscher Akademischer Austauschdienst (DAAD,Germany).

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