Eye State Identification Based on Discrete Wavelet Transforms

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UDC.coleccionInvestigaciónes_ES
UDC.departamentoEnxeñaría de Computadoreses_ES
UDC.grupoInvGrupo de Tecnoloxía Electrónica e Comunicacións (GTEC)es_ES
UDC.issue11es_ES
UDC.journalTitleApplied Scienceses_ES
UDC.volume11es_ES
dc.contributor.authorLaport, Francisco
dc.contributor.authorCastro-Castro, Paula-María
dc.contributor.authorDapena, Adriana
dc.contributor.authorVázquez Araújo, Francisco Javier
dc.contributor.authorFresnedo, Óscar
dc.date.accessioned2021-07-09T16:07:27Z
dc.date.available2021-07-09T16:07:27Z
dc.date.issued2021
dc.description.abstract[Abstract]: We present a prototype to identify eye states from electroencephalography signals captured from one or two channels. The hardware is based on the integration of low-cost components, while the signal processing algorithms combine discrete wavelet transform and linear discriminant analysis. We consider different parameters: nine different wavelets and two features extraction strategies. A set of experiments performed in real scenarios allows to compare the performance in order to determine a configuration with high accuracy and short response delay.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2020/15es_ES
dc.description.sponsorshipXunta de Galicia; ED431G2019/01es_ES
dc.description.sponsorshipXunta de Galicia; ED481A-2018/156es_ES
dc.description.sponsorshipThis work has been funded by the Xunta de Galicia (by grant ED431C 2020/15 and grant ED431G2019/01 to support the Centro de Investigación de Galicia “CITIC”), the Agencia Estatal de Investigación of Spain (by grants RED2018-102668-T and PID2019-104958RB-C42) and ERDF funds of the EU (FEDER Galicia & AEI/FEDER, UE); and the predoctoral Grant No. ED481A-2018/156 (Francisco Laport).
dc.identifier.citationLaport, F.; Castro, P.M.; Dapena, A.; Vazquez-Araujo, F.J.; Fresnedo, O. Eye State Identification Based on Discrete Wavelet Transforms. Appl. Sci. 2021, 11, 5051. https://doi.org/10.3390/app11115051es_ES
dc.identifier.doi10.3390/app11115051
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/2183/28175
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018-102668-T/ES/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104958RB-C42/ES/AVANCES EN CODIFICACION Y PROCESADO DE SEÑAL PARA LA SOCIEDAD DIGITAL
dc.relation.urihttps://doi.org/10.3390/app11115051es_ES
dc.rightsAtribución 4.0 International (CC BY 4.0)es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectDiscrete wavelet transformses_ES
dc.subjectDWTes_ES
dc.subjectElectroencephalographyes_ES
dc.subjectEEGes_ES
dc.subjectLinear discriminant analysises_ES
dc.subjectLDAes_ES
dc.subjectOcular stateses_ES
dc.titleEye State Identification Based on Discrete Wavelet Transformses_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery53b7aaca-4173-401b-94f9-37275a0a17b4

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