ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: Optimizing BMI learning and performance

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dc.contributor.authorSoekadar, Surjo R.
dc.contributor.authorWitkowski, Matthias
dc.contributor.authorMellinger, Jürgen
dc.contributor.authorRamos, Ander
dc.contributor.authorBirbaumer, Niels
dc.contributor.authorCohen, Leonardo G.
dc.contributor.institutionMedical Technologies
dc.date.accessioned2024-07-24T12:10:10Z
dc.date.available2024-07-24T12:10:10Z
dc.date.issued2011-10
dc.description.abstractEvent-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning. Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training, motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p < 0.001) and improved BMI control from S1 to S5 ( p=0.012) while Group A did not. In spite of the small n, SP in Group B showed a trend for a higher BMI performance (p = 0.06) and learning was significantly better (p < 0.05). Using a homogeneous RV and graded feedback led to improved modulation of ipsilesional activity resulting in superior BMI learning relative to use of a heterogeneous RV and binary feedback.en
dc.description.sponsorshipwas supported by the Intramural Research Program (IRP) of the National In-chronization (ERD) or synchronization (ERS) into signals that Health(NIH),theGermanFederalMinistryofEducationandResearch(BMBFstituteofNeurologicalDisordersandStroke(NINDS),NationalInstituesof control external devices [2], [3]. ERD/ERS offers quantification #01GQ0831)andtheDeutscheForschungsgemeinschaft(DFG).Theworkof of stimulus-locked brain activity e.g., during motor imagery, M. Witkowski was supported by the IRP of the NINDS/NIH, the DFG and the compared to reference conditions (RC). Easiness of use made BMBF(01GQ0831).TheworkofN.BirbaumerwassupportedbytheBMBF SMR-ERD, which reflects processing within the sensorimotor theNINDS/NIHandtheCenterforNeuroscienceandRegenerativeMedicine,(01GQ0831)andtheDFG.TheworkofL.G.CohenissupportedbytheIRPof cortex [4], an ideal candidate to drive online BMI systems in UniformedServicesUniversityofHealthSciences,Bethesda,MD. the context of neurorehabilitation [5].
dc.description.statusPeer reviewed
dc.format.extent8
dc.identifier.citationSoekadar , S R , Witkowski , M , Mellinger , J , Ramos , A , Birbaumer , N & Cohen , L G 2011 , ' ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation : Optimizing BMI learning and performance ' , IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol. 19 , no. 5 , 6035989 , pp. 542-549 . https://doi.org/10.1109/TNSRE.2011.2166809
dc.identifier.doi10.1109/TNSRE.2011.2166809
dc.identifier.issn1534-4320
dc.identifier.urihttps://hdl.handle.net/11556/4051
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=80054083441&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.relation.projectIDBMBFstituteofNeurologicalDisordersandStroke
dc.relation.projectIDIntramural Research Program
dc.relation.projectIDNational Institutes of Health, NIH
dc.relation.projectIDNational Institute of Neurological Disorders and Stroke, NINDS, 01GQ0831-ZIANS002978
dc.relation.projectIDDeutsche Forschungsgemeinschaft, DFG, 67560623
dc.relation.projectIDInternational Foundation for Research in Paraplegia, IRP
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsBrain-machine nterface
dc.subject.keywordsevent-related desynchronization
dc.subject.keywordsneurorehabilitation
dc.subject.keywordsstroke
dc.subject.keywordsInternal Medicine
dc.subject.keywordsGeneral Neuroscience
dc.subject.keywordsBiomedical Engineering
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: Optimizing BMI learning and performanceen
dc.typejournal article
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