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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorAbu-Dakka, Fares J.
dc.contributor.otherOmari, Sara
dc.contributor.otherOmari, Adil
dc.contributor.otherAbderrahim, Mohamed
dc.date.accessioned2024-10-16T15:14:55Z
dc.date.available2024-10-16T15:14:55Z
dc.date.issued2024
dc.identifier.issn2313-7673en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178133en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6667
dc.description.abstractIndividuals grappling with severe central nervous system injuries often face significant challenges related to sensorimotor function and communication abilities. In response, brain–computer interface (BCI) technology has emerged as a promising solution by offering innovative interaction methods and intelligent rehabilitation training. By leveraging electroencephalographic (EEG) signals, BCIs unlock intriguing possibilities in patient care and neurological rehabilitation. Recent research has utilized covariance matrices as signal descriptors. In this study, we introduce two methodologies for covariance matrix analysis: multiple tangent space projections (M-TSPs) and Cholesky decomposition. Both approaches incorporate a classifier that integrates linear and nonlinear features, resulting in a significant enhancement in classification accuracy, as evidenced by meticulous experimental evaluations. The M-TSP method demonstrates superior performance with an average accuracy improvement of 6.79% over Cholesky decomposition. Additionally, a gender-based analysis reveals a preference for men in the obtained results, with an average improvement of 9.16% over women. These findings underscore the potential of our methodologies to improve BCI performance and highlight gender-specific performance differences to be examined further in our future studies.en
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2024 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectinterfacesen
dc.subjectmotor imageryen
dc.subjecttangent spaceen
dc.subjectgender-based analysisen
dc.titleEEG motor imagery classification: tangent space with gate-generated weight classifieren
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceBiomimeticsen
local.contributor.groupRobótica y automatizaciónes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/biomimetics9080459en
local.contributor.otherinstitutionhttps://ror.org/03ths8210en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en
oaire.funderNameGobierno Vascoen
oaire.funderNameGobierno Vascoen
oaire.funderIdentifierhttps://ror.org/00pz2fp31 / http://data.crossref.org/fundingdata/funder/10.13039/501100003086en
oaire.fundingStreamElkartek 2022en
oaire.fundingStreamElkartek 2023en
oaire.awardNumberKK-2022-00024en
oaire.awardNumberKK-2023-00055en
oaire.awardTitleProducción Fluída y Resiliente para la Industria inteligente (PROFLOW)en
oaire.awardTitleTecnologías de Inteligencia Artificial para la percepción visual y háptica y la planificación y control de tareas de manipulación (HELDU)en
oaire.awardURISin informaciónen
oaire.awardURISin informaciónen


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