Visually evoked potential for EEG biometrics using convolutional neural network

In this paper we investigate the performance of electroencephalographic (EEG) signals, elicited by means of visual stimuli, for biometric identification. A deep learning method such as convolutional neural network (CNN), is used for automatic discriminative feature extraction and individual identification. Experiments are performed on a longitudinal database comprising of EEG data acquired from 40 subjects over two distinct sessions separated by a week time. The experimental results testify the existence of repeatable discriminative characteristics in individuals' EEG signals.