A New Approach for EEG-Based Biometric Authentication Using Auditory Stimulation

In this paper, a new approach is followed for the human recognition task using brainwave responses to auditory stimulation. A system based on this class of brainwaves benefits extra features over conventional traits being more secure, harder to spoof, and cancelable. For this purpose, EEG signals were recorded from 21 subjects while listening to modulated auditory tones in a single- and two-session setups. Three different types of features were evaluated based on the energy and the entropy estimation of the EEG sub-band rhythms using narrow band Gaussian filtering and wavelet packet decomposition. These features are classified using discriminant analysis in identification and verification modes of authentication. Based on the achieved results, high recognition rates up to 97.18% and low error rates down to 4.3% were achieved in single session setup. Moreover, in a two-session setup, the proposed system in this paper is shown to be more time-permanent in comparison to previous works.