EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm

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.

Automated summary

The electroencephalogram (EEG) has great potential for user identification, but selecting which electrodes to use is a challenging task. This study introduces a new algorithm that selects the most representative electrodes using optimization methods, and experimental results show its accuracy.