Real-time EEG-based person authentication system using face rapid serial visual presentation

As a new biometric, the Electroencephalogram (EEG) signal has the advantages of invisibility, non-clonability, and non-coercion compare to traditional biometrics. However, the real-time and stability are the difficulties that the current EEG-based person authentication systems face. In this paper, we design a real-time and stable person authentication system using EEG signals, which are elicited by self- and non-self-face rapid serial visual presentation (RSVP). Convolutional neural network (CNN) is applied to dig the specific feature of different individuals. The mean accuracy of 85.03% and 91.27% is achieved with the login time of 3 seconds and 6 seconds respectively, which illustrates the precision and real-time of the system.