Steady-State Visual Evoked Potentials for EEG-Based Biometric Identification

Abstract

In this paper we propose a biometric recognition system based on steady-state visual evoked potentials (SSVEPs), exploiting brain signals elicited by repetitive stimuli having a constant frequency as identifiers. EEG responses to SSVEP stimuli flickering at different frequencies are recorded, and both mel-frequency cepstral coefficients (MFCCs) and autoregressive (AR) reflection coefficients are used as discriminative features of the enrolled users. An analysis of the permanence across time of the brain response to SSVEP stimuli is also performed, by exploiting EEG data acquired in sessions disjoint in time. The employed database is composed by EEG recordings taken from 25 healthy subjects during two different sessions with 15 day average distance between them. The results show that good recognition performance and a high level of permanence can be reached exploiting the proposed method.