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EEG Channel Selection Based User Identification via Improved Flower Pollination Algorithm
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DOI：10.3390/s22062092.png)
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Abstract
En 中文
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.
Keywords:
EEG
biometric
beta-hill climbing
flower pollination algorithm
feature selection
auto-repressive
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