Show simple item record

dc.contributor.authorJorajuría, Tania
dc.contributor.authorNikulin, Vadim V.
dc.contributor.authorKapralov, Nikolai
dc.contributor.authorGómez, Marisol
dc.contributor.authorVidaurre, Carmen
dc.date.accessioned2024-04-23T13:17:41Z
dc.date.available2024-04-23T13:17:41Z
dc.date.issued2023
dc.identifier.citationT. Jorajuría, V. V. Nikulin, N. Kapralov, M. Gómez and C. Vidaurre, "MEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 4931-4941, 2023, doi: 10.1109/TNSRE.2023.3339612.es_ES
dc.identifier.citationIEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.identifier.issn1534-4320
dc.identifier.urihttp://hdl.handle.net/10810/66863
dc.descriptionPublished on 5 December 2023es_ES
dc.description.abstractPredicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called MEANSP to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. MEANSP has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm.es_ES
dc.description.sponsorshipThis work was supported in part by the Basque Government under Grant BERC 2022-2025 and in part by the Spanish State Research Agency through Basque Center On Cognition, Brain and Language (BCBL) Severo Ochoa Excellence Accreditation under Grant CEX2020-001010/AEI/ 10.13039/501100011033. The work of Carmen Vidaurre was supported in part by the Spanish Ministry of Research and Innovation under Grant PID2020-118829RB-100, in part by Diputacion Foral de Gipuzkoa (DFG) Brain2Move Project, in part by DFG Neurocog Project, and in part by Ikerbasque. (Corresponding author: Tania Jorajuría.) Tania Jorajuría and Marisol Gómez are with the Department of Statistics, Computer Science and Mathematics, Universidad Pública de Navarra, 31006 Pamplona, Spain (e-mail: tania.jorajuria@unavarra.es; marisol@unavarra.es). Vadim V. Nikulin is with the Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany (e-mail: nikulin@cbs.mpg.de). Nikolai Kapralov is with the International Max Planck Research School NeuroCom, 04103 Leipzig, Germany, and also with the Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany (e-mail: kapralov@cbs.mpg.de). Carmen Vidaurre was with TECNALIA, Basque Research and Technology Alliance (BRTA), 20009 San Sebastian, Spain. She is now with Ikerbasque, Basque Foundation for Science, 48009 Bilbao, Spain, also with BCBL, Basque Center on Cognition Brain and Language, 20009 San Sebastián, Spain, and also with BIFOLD, Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany (e-mail: cvidaurre@bcbl.eu)es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relationinfo:eu-repo/grantAgreement/GV/BERC2022-2025es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/CEX2020-001010-Ses_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/PID2020-118829RB-100es_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectElectroencephalographyes_ES
dc.subjectCorrelationes_ES
dc.subjectElectrodeses_ES
dc.subjectTask analysises_ES
dc.subjectSynchronizationes_ES
dc.subjectBrain modelinges_ES
dc.subjectLaplace equationses_ES
dc.subjectBrain–computer interface (BCI)es_ES
dc.subjectsensorimotor rhythms (SMRs)es_ES
dc.subjectcross-frequency couplinges_ES
dc.subjectperformance predictores_ES
dc.subjectBCI inefficiencyes_ES
dc.titleMEANSP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI?es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.es_ES
dc.relation.publisherversionhttps://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7333es_ES
dc.identifier.doi10.1109/TNSRE.2023.3339612


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record