EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm

Sensors

Mohammed Azmi Al-Betar¹  Karrar Hameed Abdulkareem²  Pattaraporn Khuwuthyakorn³  Orawit Thinnukool³  Mazin Abed Mohammed⁴  Seifedine Kadry⁵  João P. Papa⁶  Zaid Abdi Alkareem Alyasseri⁷  Osama Ahmad Alomari⁸ 

[1] Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 20550, United Arab Emirates;College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq;College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand;College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq;Department of Applied Data Science, Norrof University College, 4608 Kristiansand, Norway;Department of Computing, UNESP—São Paulo State University, Bauru 19060-560, Brazil;ECE Department, Faculty of Engineering, University of Kufa, Najaf 54001, Iraq;MLALP Research Group, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates;

关键词: EEG;    biometric;    β-hill climbing;    flower pollination algorithm;    feature selection;    auto-repressive;   

DOI  :  10.3390/s22062092

来源: DOAJ

【 摘 要 】

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 $\beta$-Hill Climbing optimizer called FPA$\beta$-hc. The performance of the FPA$\beta$-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPA$\beta$-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods.

【 授权许可】

Unknown