Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface

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 electroenc...

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Autores: Fumanal Idocin, Javier, Takáč, Zdenko, Fernández Fernández, Francisco Javier, Sanz Delgado, José Antonio, Goyena Baroja, Harkaitz, Lin, Chin-Teng, Wang, Yu-Kai, Bustince Sola, Humberto
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2021
País:España
Recursos:Universidad Pública de Navarra
Repositório:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/42913
Acesso em linha:https://hdl.handle.net/2454/42913
Access Level:Acceso aberto
Palavra-chave:Electroencephalography
Brain-computer interface
Moderate deviations
Interval-valued aggregation
Motor imagery
Admissible orders
Classification
Signal processing
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spelling Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interfaceFumanal Idocin, JavierTakáč, ZdenkoFernández Fernández, Francisco JavierSanz Delgado, José AntonioGoyena Baroja, HarkaitzLin, Chin-TengWang, Yu-KaiBustince Sola, HumbertoElectroencephalographyBrain-computer interfaceModerate deviationsInterval-valued aggregationMotor imageryAdmissible ordersClassificationSignal processingIn 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.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.IEEEEstatistika, Informatika eta MatematikaInstitute of Smart Cities - ISCEstadística, Informática y Matemáticas2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2454/42913reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00© 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.info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/429132026-06-17T12:41:47Z
dc.title.none.fl_str_mv Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
title Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
spellingShingle Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
Fumanal Idocin, Javier
Electroencephalography
Brain-computer interface
Moderate deviations
Interval-valued aggregation
Motor imagery
Admissible orders
Classification
Signal processing
title_short Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
title_full Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
title_fullStr Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
title_full_unstemmed Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
title_sort Interval-valued aggregation functions based on moderate deviations applied to motor-imagery-based brain computer interface
dc.creator.none.fl_str_mv Fumanal Idocin, Javier
Takáč, Zdenko
Fernández Fernández, Francisco Javier
Sanz Delgado, José Antonio
Goyena Baroja, Harkaitz
Lin, Chin-Teng
Wang, Yu-Kai
Bustince Sola, Humberto
author Fumanal Idocin, Javier
author_facet Fumanal Idocin, Javier
Takáč, Zdenko
Fernández Fernández, Francisco Javier
Sanz Delgado, José Antonio
Goyena Baroja, Harkaitz
Lin, Chin-Teng
Wang, Yu-Kai
Bustince Sola, Humberto
author_role author
author2 Takáč, Zdenko
Fernández Fernández, Francisco Javier
Sanz Delgado, José Antonio
Goyena Baroja, Harkaitz
Lin, Chin-Teng
Wang, Yu-Kai
Bustince Sola, Humberto
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Estatistika, Informatika eta Matematika
Institute of Smart Cities - ISC
Estadística, Informática y Matemáticas
dc.subject.none.fl_str_mv Electroencephalography
Brain-computer interface
Moderate deviations
Interval-valued aggregation
Motor imagery
Admissible orders
Classification
Signal processing
topic Electroencephalography
Brain-computer interface
Moderate deviations
Interval-valued aggregation
Motor imagery
Admissible orders
Classification
Signal processing
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/42913
url https://hdl.handle.net/2454/42913
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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