EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm.

Autor: Alyasseri ZAA; ECE Department, Faculty of Engineering, University of Kufa, Najaf 54001, Iraq.; Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf 54001, Iraq., Alomari OA; MLALP Research Group, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates., Papa JP; Department of Computing, UNESP-São Paulo State University, Bauru 19060-560, Brazil., Al-Betar MA; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 20550, United Arab Emirates.; Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, Irbid 21110, Jordan., Abdulkareem KH; College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq., Mohammed MA; College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq., Kadry S; Department of Applied Data Science, Norrof University College, 4608 Kristiansand, Norway., Thinnukool O; College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand., Khuwuthyakorn P; College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand.
Jazyk: angličtina
Zdroj: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Mar 08; Vol. 22 (6). Date of Electronic Publication: 2022 Mar 08.
DOI: 10.3390/s22062092
Abstrakt: The 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.
Databáze: MEDLINE
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