EEG Channel Selection for Person Identification Using Binary Grey Wolf Optimizer

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFaculty of Information Science and Technology-
Autor(es): dc.contributorInformation Technology Research and Development Center (ITRDC)-
Autor(es): dc.contributorMLALP Research Group-
Autor(es): dc.contributorCollege of Engineering and Information Technology-
Autor(es): dc.contributorCentre for Artificial Intelligence Research and Optimisation-
Autor(es): dc.contributorYonsei Frontier Laboratory-
Autor(es): dc.contributorAl-Huson-
Autor(es): dc.contributorITRDC-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorAlyasseri, Zaid Abdi Alkareem-
Autor(es): dc.creatorAlomari, Osama Ahmad-
Autor(es): dc.creatorMakhadmeh, Sharif Naser-
Autor(es): dc.creatorMirjalili, Seyedali-
Autor(es): dc.creatorAl-Betar, Mohammed Azmi-
Autor(es): dc.creatorAbdullah, Salwani-
Autor(es): dc.creatorAli, Nabeel Salih-
Autor(es): dc.creatorPapa, Joao P.-
Autor(es): dc.creatorRodrigues, Douglas-
Autor(es): dc.creatorAbasi, Ammar Kamal-
Data de aceite: dc.date.accessioned2025-08-21T15:30:13Z-
Data de disponibilização: dc.date.available2025-08-21T15:30:13Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ACCESS.2021.3135805-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/234066-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/234066-
Descrição: dc.descriptionElectroencephalogram signals (EEG) have provided biometric identification systems with great capabilities. Several studies have shown that EEG introduces unique and universal features besides specific strength against spoofing attacks. Essentially, EEG is a graphic recording of the brain’s electrical activity calculated by sensors (electrodes) on the scalp at different spots, but their best locations are uncertain. In this paper, the EEG channel selection problem is formulated as a binary optimization problem, where a binary version of the Grey Wolf Optimizer (BGWO) is used to find an optimal solution for such an NP-hard optimization problem. Further, a Support Vector Machine classifier with a Radial Basis Function kernel (SVM-RBF) is then considered for EEG-based biometric person identification. For feature extraction purposes, we examine three different auto-regressive coefficients. A standard EEG motor imagery dataset is employed to evaluate the proposed method, including four criteria: (i) Accuracy, (ii) F-Score, (iii) Recall, and (v) Specificity. In the experimental results, the proposed method (named BGWO-SVM) obtained 94.13% accuracy using only 23 sensors with 5 auto-regressive coefficients. Besides, BGWO-SVM finds electrodes not too close to each other to capture relevant information all over the head. As concluding remarks, BGWO-SVM achieved the best results concerning the number of selected channels and competitive classification accuracies against other meta-heuristics algorithms.-
Descrição: dc.descriptionUniversiti Kebangsaan Malaysia Center for Artificial Intelligence Faculty of Information Science and Technology, Bangi-
Descrição: dc.descriptionUniversity of Kufa Information Technology Research and Development Center (ITRDC)-
Descrição: dc.descriptionUniversity of Sharjah MLALP Research Group-
Descrição: dc.descriptionAjman University Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology-
Descrição: dc.descriptionTorrens University Australia Fortitude Valley Centre for Artificial Intelligence Research and Optimisation-
Descrição: dc.descriptionYonsei University Yonsei Frontier Laboratory-
Descrição: dc.descriptionAl-Huson University College Al-Balqa Applied University Al-Huson Department of Information Technology-
Descrição: dc.descriptionUniversity of Kufa ITRDC-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Descrição: dc.descriptionSão Paulo State University Department of Computing-
Formato: dc.format10500-10513-
Idioma: dc.languageen-
Relação: dc.relationIEEE Access-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAuthentication-
Palavras-chave: dc.subjectElectrodes-
Palavras-chave: dc.subjectElectroencephalography-
Palavras-chave: dc.subjectIris recognition-
Palavras-chave: dc.subjectSensors-
Palavras-chave: dc.subjectSupport vector machines-
Palavras-chave: dc.subjectVisualization-
Título: dc.titleEEG Channel Selection for Person Identification Using Binary Grey Wolf Optimizer-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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