Person Identification Using Electroencephalographic Signals Evoked by Visual Stimuli

The biometrics contains emerging methods for human identification. As advances in technology, conventional techniques using fingerprint or iris have the risk of being duplicated. In this work we utilize the inter-subject differences in the electroencephalographic (EEG) signals evoked by visual stimuli for person identification. The identification procedure is divided into classification and verification phases. For our classification system, it is based on the supervised classification method with support vector machine. During the classification phase, we extract the representative information from the EEG signals of each subject and construct a multi-class classifier. The best-matching candidate is further confirmed in the verification phase by using a binary classifier. The methods of feature extraction include dimension reduction and time-frequency analysis. Moreover, we try to correct those misclassified data through the iterative verification that depends on the confidence values of SVM classifier, which is a confidence level of classification. According to our experiments in which 18 subjects were recruited, the proposed method can achieve 97.25% identification rate. The results revealed that EEG data with individual differences can reach a high accuracy in person identification. Combining classification with verification, the reliability of the system can be increased. The correlation values of EEG signals between different subjects is lower than those of EEG signals acquired at different days for the same subject. This finding suggests that the characteristics of EEG has low intra-subject variability but high inter-subject variability and it is stable over time. The correlation values may also explain why some subjects apt to be misclassified when they have high correlation values to others. Our experimental results demonstrated that the proposed methods have great potentials for identifying individuals in daily life applications.