The electroencephalogram (EEG) signal represents a subject’s specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.

Electroencephalogram (EEG) signals, which originate from neurons in the brain, have drawn considerable interests in identity authentication. In this paper, a face image-based rapid serial visual presentation (RSVP) paradigm for identity authentication is proposed. This paradigm combines two kinds of biometric trait, face and EEG, together to evoke more specific and stable traits for authentication. The event-related potential (ERP) components induced by self-face and non-self-face (including familiar and not familiar) are investigated, and significant differences are found among different situations. On the basis of this, an authentication method based on Hierarchical Discriminant Component Analysis (HDCA) and Genetic Algorithm (GA) is proposed to build subject-specific model with optimized fewer channels. The accuracy and stability over time are evaluated to demonstrate the effectiveness and robustness of our method. The averaged authentication accuracy of 94.26% within 6 s can be achieved by our proposed method. For a 30-day averaged time interval, our method can still reach the averaged accuracy of 88.88%. Experimental results show that our proposed framework for EEG-based identity authentication is effective, robust, and stable over time.