Individual identification by resting-state EEG using common dictionary learning

Abstract

Recently, a number of biometric methods to identify individuals based on personal characteristics, such as fingerprints and irises, have been developed. Individual identification based on electroencephalography (EEG) measurements is one of the safe identification techniques to prevent spoofing. In this study, we propose to employ common dictionary learning, which was formerly presented by Morioka et al. [1] aiming at performing subject-transfer decoding, for extracting features for EEG-based individual identification. Using the proposed method, though a classifier was trained based on the EEG signals during the selective spatial attention task, we found each test subject was almost perfectly identified out of 40 based on its resting-state EEG signals.