Concealable Biometric-based Continuous User Authentication System An EEG Induced Deep Learning Model

This paper introduces a lightweight, low-cost, easy-to-use, and unobtrusive continuous user authentication system based on concealable biometric signals. The proposed authentication model continuously verifies a user's identity throughout the user session while s/he watches a video or performs free-text typing on his/her desktop/laptop keyboard. The authentication model utilizes unobtrusively recorded electroencephalogram (EEG) signals and learns the user's unique biometric signature based on his/her brain activity.Our work has multifold impact in the area of EEG-based authentication: (1) a comprehensive study and a comparative analysis of a wide range of extracted features are presented. These features are categorized based on the EEG electrodes placement position on the user's head, (2) an optimal feature subset is constructed using a minimal number of EEG electrodes, (3) a deep neural network-based user authentication model is presented that utilizes the constructed optimal feature subset, and (4) a detailed experimental analysis on a publicly available EEG dataset of 26 volunteer participants is presented.Our experimental results show that the proposed authentication model could achieve an average Equal Error Rate (EER) of 0.137%. Although a thorough analysis on a larger pool of subjects must be performed, our results show the viability of low-cost, lightweight EEG-based continuous user authentication systems.