EEG-based person identification through Binary Flower Pollination Algorithm

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dc.contributor.authorRodrigues, Douglas
dc.contributor.authorSilva, Gabriel F. A. [UNESP]
dc.contributor.authorPapa, Joao P. [UNESP]
dc.contributor.authorMarana, Aparecido N. [UNESP]
dc.contributor.authorYang, Xin-She
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionMiddlesex Univ
dc.date.accessioned2018-11-26T16:48:35Z
dc.date.available2018-11-26T16:48:35Z
dc.date.issued2016-11-15
dc.description.abstractElectroencephalogram (EEG) signal presents a great potential for highly secure biometric systems due to its characteristics of universality, uniqueness, and natural robustness to spoofing attacks. EEG signals are measured by sensors placed in various positions of a person's head (channels). In this work, we address the problem of reducing the number of required sensors while maintaining a comparable performance. We evaluated a binary version of the Flower Pollination Algorithm under different transfer functions to select the best subset of channels that maximizes the accuracy, which is measured by means of the Optimum-Path Forest classifier. The experimental results show the proposed approach can make use of less than a half of the number of sensors while maintaining recognition rates up to 87%, which is crucial towards the effective use of EEG in biometric applications. (C) 2016 Elsevier Ltd. All rights reserved.en
dc.description.affiliationSao Paulo State Univ, Dept Comp, Bauru, Brazil
dc.description.affiliationUniv Fed Sao Carlos, Dept Comp, Sao Carlos, SP, Brazil
dc.description.affiliationMiddlesex Univ, Sch Sci & Technol, London, England
dc.description.affiliationUnespSao Paulo State Univ, Dept Comp, Bauru, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdFAPESP: 2014/16250-9
dc.description.sponsorshipIdCNPq: 470571/2013-6
dc.description.sponsorshipIdCNPq: 306166/2014-3
dc.format.extent81-90
dc.identifierhttp://dx.doi.org/10.1016/j.eswa.2016.06.006
dc.identifier.citationExpert Systems With Applications. Oxford: Pergamon-elsevier Science Ltd, v. 62, p. 81-90, 2016.
dc.identifier.doi10.1016/j.eswa.2016.06.006
dc.identifier.fileWOS000380626000006.pdf
dc.identifier.issn0957-4174
dc.identifier.urihttp://hdl.handle.net/11449/161779
dc.identifier.wosWOS:000380626000006
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofExpert Systems With Applications
dc.relation.ispartofsjr1,271
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectMeta-heuristic
dc.subjectPattern classification
dc.subjectBiometrics
dc.subjectElectroencephalogram
dc.subjectOptimum-path forest
dc.titleEEG-based person identification through Binary Flower Pollination Algorithmen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.lattes6027713750942689[4]
unesp.author.orcid0000-0001-8231-5556[5]
unesp.author.orcid0000-0003-4861-7061[4]
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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