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dc.creatorKo, Li-Weies_ES
dc.creatorLu, Yi-Chenes_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.creatorChang, Yu-Chenges_ES
dc.creatorChang, Yanges_ES
dc.creatorFernández Fernández, Francisco Javieres_ES
dc.creatorWang, Yu-Kaies_ES
dc.creatorSanz Delgado, José Antonioes_ES
dc.creatorPereira Dimuro, Graçalizes_ES
dc.creatorLin, Chin-Tenges_ES
dc.date.accessioned2020-01-13T13:09:23Z
dc.date.available2020-01-13T13:09:23Z
dc.date.issued2019
dc.identifier.citationL. Ko et al., 'Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface,' in IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 96-106, Feb. 2019.en
dc.identifier.issn1556-603X
dc.identifier.urihttps://hdl.handle.net/2454/36054
dc.description.abstractBrain–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.en
dc.description.sponsorshipThis 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-.en
dc.format.extent25 p.
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE Computational Intelligence Magazine, 14 (1), 96-106, 2019en
dc.rights© 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 worken
dc.subjectBrain-computer interfaceen
dc.subjectElectroencephalography (EEG)en
dc.subjectFuzzy fusionen
dc.subjectFuzzy integralsen
dc.subjectMotor imageryen
dc.titleMultimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interfaceen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeArtículo / Artikuluaes
dc.contributor.departmentUniversidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticases_ES
dc.contributor.departmentNafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Sailaeu
dc.contributor.departmentUniversidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Citieses_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.identifier.doi10.1109/MCI.2018.2881647
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-Pen
dc.relation.publisherversionhttps://doi.org/10.1109/MCI.2018.2881647
dc.type.versioninfo:eu-repo/semantics/acceptedVersionen
dc.type.versionVersión aceptada / Onetsi den bertsioaes


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