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dc.contributor.authorZaid Abdi Alkareem Alyasserien_US
dc.contributor.authorOsama Ahmad Alomarien_US
dc.contributor.authorJoão P. Papaen_US
dc.contributor.authorMohammed Azmi Al-Betaren_US
dc.contributor.authorKarrar Hameed Abdulkareemen_US
dc.contributor.authorMazin Abed Mohammeden_US
dc.contributor.authorSeifedine Kadryen_US
dc.contributor.authorOrawit Thinnukoolen_US
dc.contributor.authorPattaraporn Khuwuthyakornen_US
dc.description.abstractThe electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the scalp at different places. However, selecting which electrodes should be used is a challenging task. Such a subject is formulated as an electrode selection task that is tackled by optimization methods. In this work, a new approach to select the most representative electrodes is introduced. The proposed algorithm is a hybrid version of the Flower Pollination Algorithm and β-Hill Climbing optimizer called FPAβ-hc. The performance of the FPAβ-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPAβ-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectComputer Scienceen_US
dc.subjectPhysics and Astronomyen_US
dc.titleEEG Channel Selection Based User Identification via Improved Flower Pollination Algorithmen_US
article.volume22en_US of Kufa, Information Technology Research and Development Centreen_US Universityen_US Of Anbaren_US of Kufaen_US of Sharjahen_US Universityen_US Applied Universityen_US Estadual Paulista "Júlio de Mesquita Filho"en_US Mai Universityen_US University Collegeen_US
Appears in Collections:CMUL: Journal Articles

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