EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm

• Autor(a) principal: Alyasseri, Zaid Abdi Alkareem
• Data de Publicação: 2022
• Outros Autores: Alomari, Osama Ahmad, Papa, João P. [UNESP], Al-Betar, Mohammed Azmi, Abdulkareem, Karrar Hameed, Mohammed, Mazin Abed, Kadry, Seifedine, Thinnukool, Orawit, Khuwuthyakorn, Pattaraporn
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/s22062092; http://hdl.handle.net/11449/234242
• topic: Auto-repressive; Biometric; EEG; Feature selection; Flower pollination algorithm; β-hill climbing

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.