Using rapid visually evoked EEG activity for person identification

We investigate the potential of using EEG recordings of observers performing a rapid visual categorization task for person identification. We examine a 0.5 s epoch of EEG data using machine learning techniques that, unlike most previous studies, analyze the data in a holistic manner and extracts discriminative spatio-temporal filters. The analysis of the filters suggest sparse feature representation spatially as well as temporally. The filters reveal that the neural activity that discriminates individuals is spatially localized to occipital electrodes located on the scalp above the visual cortex and temporally localized in the interval of 120-200 ms after presentation of the visual stimulus. The results demonstrate the feasibility of EEG-based person identification based on difficult perceptual tasks.