EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm
Zaid Abdi Alkareem Alyasseri,
Osama Ahmad Alomari,
João P. Papa,
Mohammed Azmi Al-Betar,
Karrar Hameed Abdulkareem,
Mazin Abed Mohammed,
Seifedine Kadry,
Orawit Thinnukool,
Pattaraporn Khuwuthyakorn
Affiliations
Zaid Abdi Alkareem Alyasseri
ECE Department, Faculty of Engineering, University of Kufa, Najaf 54001, Iraq
Osama Ahmad Alomari
MLALP Research Group, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
João P. Papa
Department of Computing, UNESP—São Paulo State University, Bauru 19060-560, Brazil
Mohammed Azmi Al-Betar
Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 20550, United Arab Emirates
Karrar Hameed Abdulkareem
College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
Mazin Abed Mohammed
College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq
Seifedine Kadry
Department of Applied Data Science, Norrof University College, 4608 Kristiansand, Norway
Orawit Thinnukool
College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
Pattaraporn Khuwuthyakorn
College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand
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.
