A Sparse Representation Classification Framework for Person Identification and Verification Using Neurophysiological Signals

Brain biometrics has received increasing attention from the scientific community due to its unique properties in comparison to traditional biometric methods. Many studies have shown that EEG features are distinct among individuals. SSVEP signals, generated by stationary localized sources and distributed sources in the parietal and occipital regions of the brain, serve as a reliable basis for biometrics. In this study, we present a novel approach that leverages the spatial patterns of brain responses elicited by visual stimulation at specific frequencies. Specifically, we propose integrating common spatial patterns with Sparse Representation Classification (SRC) frameworks for person identification and verification. The use of common spatial patterns enables the design of personalized spatial filters, which play a crucial role in constructing the dictionary used by SRC frameworks. We conducted extensive evaluations of the proposed method, comparing it with several traditional approaches using two SSVEP datasets. Our analysis also explored a broad range of flickering frequencies in the SSVEP experiments. The results from these datasets demonstrated the effectiveness of our approach for person identification and verification, achieving an average correct recognition rate above 90% across various visual stimulus frequencies and short durations of electrophysiological signals.