Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface

Brain–computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery–based brain–computer interfaces are popular because they avoid unnecessary external stimulus. Al...

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Autores: Ko, Li-Wei, Lu, Yi-Chen, Bustince Sola, Humberto, Chang, Yu-Cheng, Chang, Yang, Fernández Fernández, Francisco Javier, Wang, Yu-Kai, Sanz Delgado, José Antonio, Pereira Dimuro, Graçaliz, Lin, Chin-Teng
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Recursos:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/36054
Acesso em linha:https://hdl.handle.net/2454/36054
Access Level:acceso abierto
Palavra-chave:Brain-computer interface
Electroencephalography (EEG)
Fuzzy fusion
Fuzzy integrals
Motor imagery
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spelling Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interfaceKo, Li-WeiLu, Yi-ChenBustince Sola, HumbertoChang, Yu-ChengChang, YangFernández Fernández, Francisco JavierWang, Yu-KaiSanz Delgado, José AntonioPereira Dimuro, GraçalizLin, Chin-TengBrain-computer interfaceElectroencephalography (EEG)Fuzzy fusionFuzzy integralsMotor imageryBrain–computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery–based brain–computer interfaces are popular because they avoid unnecessary external stimulus. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain–computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain–computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain–computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain–computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain–computer interface commands.This work was supported in part by the Australian Research Council (ARC) under discovery grant DP180100670 and DP180100656, and in part by the Spanish Ministry of Science under discovery grant TIN2016-77356-P(MINECO, FEDER, UE). This work was also particularly supported by the Ministry of Education through the SPROUT Project - Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) of National Chiao Tung University, Taiwan, and supported in part by the Ministry of Science and Technology (MOST), Taiwan, under Contract MOST 107-2221-E-009-150-.IEEEEstatistika, Informatika eta MatematikaInstitute of Smart Cities - ISCEstadística, Informática y Matemáticas2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2454/36054reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglésinfo:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-P© 2019 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 workinfo:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/360542026-06-17T12:41:47Z
dc.title.none.fl_str_mv Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
title Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
spellingShingle Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
Ko, Li-Wei
Brain-computer interface
Electroencephalography (EEG)
Fuzzy fusion
Fuzzy integrals
Motor imagery
title_short Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
title_full Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
title_fullStr Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
title_full_unstemmed Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
title_sort Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface
dc.creator.none.fl_str_mv Ko, Li-Wei
Lu, Yi-Chen
Bustince Sola, Humberto
Chang, Yu-Cheng
Chang, Yang
Fernández Fernández, Francisco Javier
Wang, Yu-Kai
Sanz Delgado, José Antonio
Pereira Dimuro, Graçaliz
Lin, Chin-Teng
author Ko, Li-Wei
author_facet Ko, Li-Wei
Lu, Yi-Chen
Bustince Sola, Humberto
Chang, Yu-Cheng
Chang, Yang
Fernández Fernández, Francisco Javier
Wang, Yu-Kai
Sanz Delgado, José Antonio
Pereira Dimuro, Graçaliz
Lin, Chin-Teng
author_role author
author2 Lu, Yi-Chen
Bustince Sola, Humberto
Chang, Yu-Cheng
Chang, Yang
Fernández Fernández, Francisco Javier
Wang, Yu-Kai
Sanz Delgado, José Antonio
Pereira Dimuro, Graçaliz
Lin, Chin-Teng
author2_role author
author
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 Brain-computer interface
Electroencephalography (EEG)
Fuzzy fusion
Fuzzy integrals
Motor imagery
topic Brain-computer interface
Electroencephalography (EEG)
Fuzzy fusion
Fuzzy integrals
Motor imagery
description Brain–computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery–based brain–computer interfaces are popular because they avoid unnecessary external stimulus. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain–computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain–computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain–computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain–computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain–computer interface commands.
publishDate 2019
dc.date.none.fl_str_mv 2019
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/2454/36054
url https://hdl.handle.net/2454/36054
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-P
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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