Person identification using EEG channel selection with hybrid flower pollination algorithm

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dc.contributor.authorAlyasseri, Zaid Abdi Alkareem
dc.contributor.authorKhader, Ahamad Tajudin
dc.contributor.authorAl-Betar, Mohammed Azmi
dc.contributor.authorAlomari, Osama Ahmad
dc.date.accessioned2020-08-08T21:12:04Z
dc.date.available2020-08-08T21:12:04Z
dc.date.issued2020en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractRecently, electroencephalogram (EEG) signal presents a great potential for a new biometric system to deal with a cognitive task. Several studies defined the EEG with uniqueness features, universality, and natural robustness that can be used as a new track to prevent spoofing attacks. The EEG signals are the graphical recording of the brain electrical activities which can be measured by placing electrodes (channels) in various positions of the scalp. With a large number of channels, some channels have very important information for biometric system while others not. The channel selection problem has been recently formulated as an optimisation problem and solved by optimisation techniques. This paper proposes hybrid optimisation techniques based on binary flower pollination algorithm (FPA) and beta-hill climbing (called FPA beta-hc) for selecting the most relative EEG channels (i.e., features) that come up with efficient accuracy rate of personal identification. Each EEG signals with three different groups of EEG channels have been utilized (i.e., time domain, frequency domain, and time-frequency domain). The FPA beta-hc is measured using a standard EEG signal dataset, namely, EEG motor movement/imagery dataset with a real world data taken from 109 persons each with 14 different cognitive tasks using 64 channels. To evaluate the performance of the FPA beta-hc, five measurement criteria are considered:accuracy (Acc), (ii) sensitivity (Sen), (iii) F-score (F_s), (v) specificity (Spe), and (iv) number of channels selected (No. Ch). The proposed method is able to identify the personals with high Acc, Sen., F_s, Spe, and less number of channels selected. Interestingly, the experimental results suggest that FPA beta-hc is able to reduce the number of channels with accuracy rate up to 96% using time-frequency domain features. For comparative evaluation, the proposed method is able to achieve results better than those produced by binary-FPA-OPF method using the same EEG motor movement/imagery datasets. In a nutshell, the proposed method can be very beneficial for effective use of EEG signals in biometric applications.en_US
dc.identifier.doi10.1016/j.patcog.2020.107393en_US
dc.identifier.issn0031-3203
dc.identifier.issn1873-5142
dc.identifier.scopus2-s2.0-85084034057en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://hdl.handle.net/11363/2341
dc.identifier.urihttps://doi.org/
dc.identifier.volume105en_US
dc.identifier.wosWOS:000539457100017en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLANDen_US
dc.relation.ispartofPATTERN RECOGNITIONen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectEEGen_US
dc.subjectBiometricen_US
dc.subjectChannel selectionen_US
dc.subjectFlower pollination algorithmen_US
dc.subjectbeta-hill climbingen_US
dc.subjectPARTICLE SWARM OPTIMIZATIONen_US
dc.subjectCLASSIFICATIONen_US
dc.subjectPATTERNSen_US
dc.titlePerson identification using EEG channel selection with hybrid flower pollination algorithmen_US
dc.typeArticleen_US

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