Convolutional Neural Networks Using Dynamic Functional Connectivity for EEG-Based Person Identification in Diverse Human States

Highly secure access control requires Swiss-cheese-type multi-layer security protocols. The use of electroencephalogram (EEG) to provide cognitive indicators for human workload and fatigue has created environments where the EEG data are well-integrated into systems, making it readily available for more forms of innovative uses including biometrics. However, most of the existing studies on EEG biometrics rely on resting state signals or require specific and repetitive sensory stimulation, limiting their uses in naturalistic settings. Moreover, the limited discriminatory power of uni-variate measures denies an opportunity to use dependences information inherent in brain regions to design more robust biometric identifiers. In this paper, we proposed a novel model for ongoing EEG biometric identification using EEG collected during a diverse set of tasks. The novelty lies in representing EEG signals as a graph based on within-frequency and cross-frequency functional connectivity estimates, and the use of graph convolutional neural network (GCNN) to automatically capture deep intrinsic structural representations from the EEG graphs for person identification. An extensive investigation was carried out to assess the robustness of the method against diverse human states, including resting states under eye-open and eye-closed conditions and active states drawn during the performance of four different tasks. We compared our method with the state-of-the-art EEG features, classifiers, and models of EEG biometrics. Results show that the representation drawn from EEG functional connectivity graphs demonstrates more robust biometric traits than direct use of uni-variate features. Moreover, the GCNN can effectively and efficiently capture discriminative traits, thus generalizing better over diverse human states.