Show simple item record

dc.creatorVidaurre, Carmenes_ES
dc.creatorJorajuría, Taniaes_ES
dc.creatorRamos Murguialday, Anderes_ES
dc.creatorMüller, Klaus Robertes_ES
dc.creatorGómez Fernández, Marisoles_ES
dc.creatorNikulin, Vadim V.es_ES
dc.date.accessioned2022-01-19T08:43:47Z
dc.date.available2022-01-19T08:43:47Z
dc.date.issued2021
dc.identifier.issn1741-2552
dc.identifier.urihttps://hdl.handle.net/2454/41814
dc.description.abstractObjective. Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements. Approach. We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop. Main results. When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances. Significance. We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.en
dc.description.sponsorshipC V was supported by MINECO-RyC-2014-15671 and PID2020-118829RB-I00. A R was supported by EU-EUROSTARS E!113550 and H2020-EICFETPROACT-2019-951910-MAIA. K R M was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) Grants funded by the Korea Government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning) and funded by the Korea Government (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University), and was partly supported by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. VVN was partly supported by the Basic Research Program of the National Research University Higher School of Economics (HSE University).en
dc.format.extent16 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherIOP Publishing
dc.relation.ispartofJournal of Neural Engineering, 18 (4),
dc.rights© 2021 The Author(s). Creative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMotor imageryen
dc.subjectBrain-computer interfacingen
dc.subjectInduced movementsen
dc.subjectNeuro-muscular electrical stimulationen
dc.subjectMotor disturbancesen
dc.subjectAfferent signalsen
dc.subjectFeedback contingencyen
dc.titleImproving motor imagery classification during induced motor perturbationsen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1088/1741-2552/ac123f
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//RYC-2014-15671/ES/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118829RB-I00/ES/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/951910en
dc.relation.publisherversionhttp://doi.org/10.1088/1741-2552/ac123f
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.type.versionVersión publicada / Argitaratu den bertsioaes


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

© 2021 The Author(s). Creative Commons Attribution 4.0 International
Except where otherwise noted, this item's license is described as © 2021 The Author(s). Creative Commons Attribution 4.0 International

El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
Logo MinisterioLogo Fecyt