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

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dc.contributor.authorVidaurre, Carmen
dc.contributor.authorRamos Murguialday, Anderes_ES
dc.contributor.authorHaufe, Stefanes_ES
dc.contributor.authorGómez Fernández, Marisol
dc.contributor.authorMüller, Klaus Robertes_ES
dc.contributor.authorNikulin, Vadim V.es_ES
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.date.accessioned2020-04-17T10:27:14Z
dc.date.available2020-10-01T23:00:13Z
dc.date.issued2019
dc.description.abstractAn important goal in Brain-Computer Interfacing (BCI) is tofind 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 MIor 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. Thesefinding 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, Russian Academic Excellence Project '5-100'. Correspondence to CV, KRM and VN.en
dc.embargo.lift2020-10-01
dc.embargo.terms2020-10-01
dc.format.extent35 p.
dc.format.mimetypeapplication/pdfen
dc.identifier.doi10.1016/j.neuroimage.2019.05.074
dc.identifier.issn1053-8119
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/36746
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofNeuroImage 199 (2019) 375-386en
dc.relation.publisherversionhttps://doi.org/10.1016/j.neuroimage.2019.05.074
dc.rights© 2019 Elsevier Inc. This manuscript version is made available under the CC-BY-NC-ND 4.0.en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMotor Imagery (MI)en
dc.subjectSensory Threshold Neuromuscular Electrical Stimulation (STM)en
dc.subjectAfferent patternsen
dc.subjectEfferent patternsen
dc.subjectBrain-Computer Interfacing (BCI) ine_x000E_ciencyen
dc.titleEnhancing sensorimotor BCI performance with assistive afferent activity: an online evaluationen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.type.versionVersión aceptada / Onetsi den bertsioaes
dspace.entity.typePublication
relation.isAuthorOfPublicationbfc272aa-95a8-45b2-ada9-5e679009a082
relation.isAuthorOfPublication71fc3a8f-62c3-41cf-bca2-eeaaa41d54af
relation.isAuthorOfPublication.latestForDiscoverybfc272aa-95a8-45b2-ada9-5e679009a082

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