Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface
| dc.contributor.author | Fumanal Idocin, Javier | |
| dc.contributor.author | Vidaurre Arbizu, Carmen | |
| dc.contributor.author | Fernández Fernández, Francisco Javier | |
| dc.contributor.author | Gómez Fernández, Marisol | |
| dc.contributor.author | Andreu-Pérez, Javier | |
| dc.contributor.author | Prasad, M. | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute for Advanced Materials and Mathematics - INAMAT2 | en |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.date.accessioned | 2024-05-22T11:46:34Z | |
| dc.date.available | 2024-05-22T11:46:34Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2024-05-22T11:31:33Z | |
| dc.description.abstract | In this paper we propose a new version of penalty-based aggregation functions, the Multi Cost Aggregation choosing functions (MCAs), in which the function to minimize is constructed using a convex combination of two relaxed versions of restricted equivalence and dissimilarity functions instead of a penalty function. We additionally suggest two different alternatives to train a MCA in a supervised classification task in order to adapt the aggregation to each vector of inputs. We apply the proposed MCA in a Motor Imagery-based Brain- Computer Interface (MI-BCI) system to improve its decision making phase. We also evaluate the classical aggregation with our new aggregation procedure in two publicly available datasets. We obtain an accuracy of 82.31% for a left vs. right hand in the Clinical BCI challenge (CBCIC) dataset, and a performance of 62.43% for the four-class case in the BCI Competition IV 2a dataset compared to a 82.15% and 60.56% using the arithmetic mean. Finally, we have also tested the goodness of our proposal against other MI-BCI systems, obtaining better results than those using other decision making schemes and Deep Learning on the same datasets. | en |
| dc.description.sponsorship | Javier Fumanal Idocin, Javier Fernandez, and Humberto Bustince's research has been supported by the project PID2019-108392GB I00 (AEI/10.13039/501100011033). Carmen Vidaurre research has been funded by the project RyC2014-15671. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Fumanal-Idocin, J., Vidaurre, C., Fernandez, J., Gómez, M., Andreu-Perez, J., Prasad, M., Bustince, H. (2024) Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface. Pattern Recognition, 145, 1-11. https://doi.org/10.1016/j.patcog.2023.109924. | es_ES |
| dc.identifier.doi | 10.1016/j.patcog.2023.109924 | |
| dc.identifier.issn | 0031-3203 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/48167 | |
| dc.language.iso | eng | en |
| dc.publisher | Elsevier | en |
| dc.relation.ispartof | Pattern Recognition (2024), vol. 145, 109924 | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RYC-2014-15671/ES/ | |
| dc.relation.publisherversion | https://doi.org/10.1016/j.patcog.2023.109924 | |
| dc.rights | © 2023 The Author(s). This is an open access article under the CC BY-NC-ND license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Brain-computer interface | en |
| dc.subject | Motor imagery | en |
| dc.subject | Penalty function | en |
| dc.subject | Aggregation functions | en |
| dc.subject | Classification | en |
| dc.subject | Signal processing | en |
| dc.title | Supervised penalty-based aggregation applied to motor-imagery based brain-computer-interface | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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