EEG biometrics for individual recognition in resting state with closed eyes

Abstract

In this paper EEG signals are employed for the purpose of automatic user recognition. Specifically the resting state with closed eyes acquisition protocol has been here used and deeply investigated by varying the employed electrodes configuration both in number and location for optimizing the recognition performance still guaranteeing sufficient user convenience. A database of 45 healthy subjects has been employed in the analysis. Autoregressive stochastic modeling and polynomial regression based classification has been applied to extracted brain rhythms in order to identify the most distinctive contributions of the different subbands in the recognition process. Our analysis has shown that significantly high recognition rates, up to 98.73%, can be achieved when using proper triplets of electrodes, which cannot be achieved by employing couple of electrodes, whereas sets of five electrodes in the central posterior region of the scalp can guarantee very high recognition performance while limiting user convenience.