Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation

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dc.contributor.authorVidaurre, C.
dc.contributor.authorRamos Murguialday, A.
dc.contributor.authorHaufe, S.
dc.contributor.authorGómez, M.
dc.contributor.authorMüller, K. R.
dc.contributor.authorNikulin, V. V.
dc.contributor.institutionMedical Technologies
dc.date.accessioned2024-07-24T12:04:01Z
dc.date.available2024-07-24T12:04:01Z
dc.date.issued2019-10-01
dc.descriptionPublisher Copyright: © 2019 Elsevier Inc.
dc.description.abstractAn important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).en
dc.description.sponsorshipThe work of CV and KRM was funded by the German Ministry for Education and Research (BMBF) under Grant 01IS14013A-E and Grant 01GQ1115, as well as by the Deutsche Forschungsgesellschaft (DFG) under Grant MU 987/19-1, MU987/14-1 and DFG MU 987/3-2 and by the EU-FP7 MUNDUS project Grant 248326. Additionally, KRM was partly supported by the German Ministry for Education and Research as Berlin Big Data Centre (01IS14013A) and Berlin Center for Machine Learning (01IS18037I). Partial funding by DFG is acknowledged (EXC 2046/1, project-ID: 390685689). This work was also supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451, No. 2017-0-01779). CV was also supported by the Spanish Ministry of Economy RYC-2014-15671. VN has been supported by the HSE Basic Research Program and the Russian Academic Excellence Project ‘5-100’. Correspondence to CV, KRM and VN. The work of CV and KRM was funded by the German Ministry for Education and Research (BMBF) under Grant 01IS14013A-E and Grant 01GQ1115 , as well as by the Deutsche Forschungsgesellschaft (DFG) under Grant MU 987/19-1 , MU987/14-1 and DFG MU 987/3-2 and by the EU-FP7 MUNDUS project Grant 248326 . Additionally, KRM was partly supported by the German Ministry for Education and Research as Berlin Big Data Centre ( 01IS14013A ) and Berlin Center for Machine Learning ( 01IS18037I ). Partial funding by DFG is acknowledged ( EXC 2046/1 , project-ID: 390685689 ). This work was also supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (No. 2017-0-00451 , No. 2017-0-01779 ). CV was also supported by the Spanish Ministry of Economy RYC-2014-15671 . VN has been supported by the HSE Basic Research Program and the Russian Academic Excellence Project ‘ 5-100 ’. Correspondence to CV, KRM and VN.
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationVidaurre , C , Ramos Murguialday , A , Haufe , S , Gómez , M , Müller , K R & Nikulin , V V 2019 , ' Enhancing sensorimotor BCI performance with assistive afferent activity : An online evaluation ' , NeuroImage , vol. 199 , pp. 375-386 . https://doi.org/10.1016/j.neuroimage.2019.05.074
dc.identifier.doi10.1016/j.neuroimage.2019.05.074
dc.identifier.issn1053-8119
dc.identifier.urihttps://hdl.handle.net/11556/3410
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85067109507&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofNeuroImage
dc.relation.projectIDDeutsche Forschungsgesellschaft, DFG MU 987/3-2-MU987/14-1-MU 987/19-1
dc.relation.projectIDEU-FP7, 248326
dc.relation.projectIDGerman Ministry for Education and Research
dc.relation.projectIDGerman Ministry for Education and Research as Berlin Big Data Centre, 01IS14013A
dc.relation.projectIDInstitute for Information & communications Technology Planning & Evaluation
dc.relation.projectIDSpanish Ministry of Economy
dc.relation.projectIDHealth and Safety Executive, HSE
dc.relation.projectIDDeutsche Forschungsgemeinschaft, DFG
dc.relation.projectIDBundesministerium für Bildung und Forschung, BMBF, 01IS14013A-E-01GQ1115
dc.relation.projectIDMinisterio de Economía y Competitividad, MEC, RYC-2014-15671
dc.relation.projectIDInstitute for Information and Communications Technology Promotion, IITP, 2017-0-01779-2017-0-00451
dc.relation.projectIDBerlin Center for Machine Learning, BZML, EXC 2046/1-390685689-01IS18037I
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsAfferent patterns
dc.subject.keywordsBrain-computer interfacing (BCI) inefficiency
dc.subject.keywordsEfferent patterns
dc.subject.keywordsMotor imagery (MI)
dc.subject.keywordsSensory threshold neuromuscular electrical stimulation (STM)
dc.subject.keywordsNeurology
dc.subject.keywordsCognitive Neuroscience
dc.subject.keywordsSDG 3 - Good Health and Well-being
dc.titleEnhancing sensorimotor BCI performance with assistive afferent activity: An online evaluationen
dc.typejournal article
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