Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
| dc.contributor.author | Fumanal Idocin, Javier | |
| dc.contributor.author | Takáč, Zdenko | |
| dc.contributor.author | Fernández Fernández, Francisco Javier | |
| dc.contributor.author | Sanz Delgado, José Antonio | |
| dc.contributor.author | Goyena Baroja, Harkaitz | |
| dc.contributor.author | Lin, Chin-Teng | |
| dc.contributor.author | Wang, Yu-Kai | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.department | Institute of Smart Cities - ISC | en |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.date.accessioned | 2022-05-10T11:00:35Z | |
| dc.date.available | 2022-06-29T23:00:15Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | In this work we develop moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data to construct interval-valued aggregation functions, and we apply these functions in two MotorImagery Brain Computer Interface (MI-BCI) systems to classify electroencephalography signals. To do so, we introduce the notion of interval-valued moderate deviation function and, in particular, we study those interval-valued moderate deviation functions which preserve the width of the input intervals. In order to apply them in a MI-BCI system, we first use fuzzy implication operators to measure the uncertainty linked to the output of each classifier in the ensemble of the system, and then we perform the decision making phase using the new interval-valued aggregation functions. We have tested the goodness of our proposal in two MI-BCI frameworks, obtaining better results than those obtained using other numerical aggregation and interval-valued OWA operators, and obtaining competitive results versus some non aggregation-based frameworks. | en |
| dc.description.sponsorship | Javier Fumanal Idocin’s, Jose Antonio Sanz’s, Javier Fernandez’s, Harkaitz Goyena’s and Humberto Bustince’s research has been supported by the project PID2019-108392GB I00 (AEI/10.13039/501100011033). Z. Takac acknowledges the support of the grant VEGA 1/0545/20. | en |
| dc.embargo.lift | 2022-06-29 | |
| dc.embargo.terms | 2022-06-29 | |
| dc.format.extent | 15 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | J. Fumanal-Idocin et al., 'Interval-valued aggregation functions based on Moderate deviations applied to Motor-Imagery-Based Brain Computer Interface,' in IEEE Transactions on Fuzzy Systems, doi: 10.1109/TFUZZ.2021.3092824. | en |
| dc.identifier.doi | 10.1109/TFUZZ.2021.3092824 | |
| dc.identifier.issn | 1941-0034 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/42913 | |
| dc.language.iso | eng | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartof | IEEE Transactions on Fuzzy Systems (2021) | en |
| 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.publisherversion | https://doi.org/10.1109/TFUZZ.2021.3092824 | |
| dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.subject | Electroencephalography | en |
| dc.subject | Brain-computer interface | en |
| dc.subject | Moderate deviations | en |
| dc.subject | Interval-valued aggregation | en |
| dc.subject | Motor imagery | en |
| dc.subject | Admissible orders | en |
| dc.subject | Classification | en |
| dc.subject | Signal processing | en |
| dc.title | Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | |
| dspace.entity.type | Publication | |
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