P300-BCI-based authentication system

Abstract

An authentication system is the system that decides whether to accept or reject the claiming identity of a person. Biometric-based authentication utilizes the individuality in human physiological and behavioral characteristics to authorize a person. Brain-signal-based authentication system is relatively new comparing to other types of biometric data. In this paper, we proposed a novel method that applies P300-based Brain Computer Interface (BCI) technique to the authentication system. The main concept for P300-BCI-based authentication is that the Oddball paradigm eliciting P300 waves is secret to the attacker. The experiments were conducted to evaluate the proposed system. The trained P300 classification model has 0.831 accuracy rate. And the proposed authentication system has 0.325 False Rejection Rate (FRR), 0.00 False Acceptation Rate (FAR) for secret-unknown attack and 0.10 FAR for secret-known attack. This study has shown that P300 wave has good potential as a biometric for highly secured authentication system.