Individual identification based on resting-state EEG

Traditional electroencephalography (EEG)-based authentication systems generally use external stimuli that require user attention and relatively long time for authentication. The aim of this study is to investigate whether EEGs measured in resting state without using external stimuli can be used to develop a biometric authentication system. Seventeen subjects participated in the experiment in which EEG data were measured while the subjects repetitively closed and opened their eyes. Changes in alpha activity (8-13 Hz) during eyes open and closed were extracted for each channel as features, and inter- and intra-subject cross-correlation was calculated for identifying each subject. Increase in alpha activity was observed for all subjects at most channels. Most importantly, spatio-spectral patterns of changed alpha activity were different between the subjects, which led to a high mean identification accuracy of 88.4 %. Our experimental results demonstrate the feasibility of the proposed authentication method based on resting state EEGs.