EEG Feature Extraction for Person Identification Using Wavelet Decomposition and Multi-Objective Flower Pollination Algorithm

In the modern life, the authentication technique for any system is considered as one of the most important and challenging tasks. Therefore, many researchers have developed traditional authentication systems to deal with our digital society. Recently, several studies showed that the brain electrical activity or electroencephalogram (EEG) signals could provide robust and unique features that can be considered as a new biometric authentication technique, given that accurate methods to decompose the signals must also be considered. This paper proposes a novel method for extracting EEG features using multi-objective flower pollination algorithm and the wavelet transform. The proposed method was applied in two scenarios for EEG signal decomposition to extract unique features from the original signals. Moreover, the proposed method is compared with the state-of-the-art techniques using different criteria with promising results.