Publication: Improving motor imagery classification during induced motor perturbations
| dc.contributor.author | Vidaurre, Carmen | |
| dc.contributor.author | Jorajuría, Tania | |
| dc.contributor.author | Ramos Murguialday, Ander | es_ES |
| dc.contributor.author | Müller, Klaus Robert | es_ES |
| dc.contributor.author | Gómez Fernández, Marisol | |
| dc.contributor.author | Nikulin, Vadim V. | es_ES |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.date.accessioned | 2022-01-19T08:43:47Z | |
| dc.date.available | 2022-01-19T08:43:47Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Objective. 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.sponsorship | C 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.extent | 16 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.1088/1741-2552/ac123f | |
| dc.identifier.issn | 1741-2552 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/41814 | |
| dc.language.iso | eng | en |
| dc.publisher | IOP Publishing | |
| dc.relation.ispartof | Journal of Neural Engineering, 18 (4), | |
| dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RYC-2014-15671/ES/ | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118829RB-I00/ES/ | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/951910 | en |
| dc.relation.publisherversion | http://doi.org/10.1088/1741-2552/ac123f | |
| dc.rights | © 2021 The Author(s). Creative Commons Attribution 4.0 International | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
| dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Motor imagery | en |
| dc.subject | Brain-computer interfacing | en |
| dc.subject | Induced movements | en |
| dc.subject | Neuro-muscular electrical stimulation | en |
| dc.subject | Motor disturbances | en |
| dc.subject | Afferent signals | en |
| dc.subject | Feedback contingency | en |
| dc.title | Improving motor imagery classification during induced motor perturbations | en |
| dc.type | info:eu-repo/semantics/article | en |
| dc.type | Artículo / Artikulua | es |
| dc.type.version | info:eu-repo/semantics/publishedVersion | en |
| dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
| dspace.entity.type | Publication | |
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