Personal Identity Verification by EEG-Based Network Representation on a Portable Device

EEG-based personal verification was investigated so far by using mainly standard non-portable device with a large number of electrodes (typically, 64) constrained to heavy headset configuration. Despite this equipment has been shown to be useful to investigate in depth EEG signal characteristics from a biomedical point of view, it may be considered less appropriate for designing real-life EEG-based biometric systems. In this work, EEG signals are collected by a portable and user-friendly device explicitly conceived for biometric applications, featured by a set of 16 channels. Investigated feature extraction algorithms are based on modelling the EEG channels as a network of mutually interacting units, which was shown to be effective for personal verification purposes when brain signals are acquired by standard EEG devices. This work shows that, even using a reduced set of channels, these approaches still remain effective. The aim of this paper is intended to stimulate research on the use of light and portable EEG headset configurations by adopting network-based representations of EEG brain signals, since a light headset represents a precondition in order to design real-life EEG-based personal verification systems