EEG-based Person Authentication Using Multi-objective Flower Pollination Algorithm

Since the past decades, the world has been transformed into a digital society, where every individual is living with a unique identifier. The primary purpose of this id is to distinguish from others and to deal with digital machines which are surrounding the world. Recently, many researchers 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 accurately methods to decompose the signals must also be considered. This paper proposes a novel method for EEG signal denoising based on the multi-objective Flower Pollination Algorithm and the Wavelet Transform (MOFPA-WT) to extract useful features from denoised signals. MOFPA-WT is tested using a standard EEG signal dataset, namely, EEG motor movement/imagery dataset, and its performance is evaluated using three criteria: (i) accuracy, (ii) true acceptance rate, and (iii) false acceptance rate. We show that the proposed method can achieve results that are comparable to the state-of-the-art ones, as well as we draw future directions towards the research area.