Comparison of different entropies as features for person authentication based on EEG signals
Person authentication is an important part to protect individual privacy in the informational society. With the development of electroencephalogram (EEG), it gradually becomes feasible using EEG signals to identify person recognition. However, the analysis of EEG signals is complex, unstable and non-linear. With this fact, non-linear analysis such as entropy would be more appropriate. In this study, four types of entropies are used to extract EEG signals features for the purpose of person authentication, and the performance of person authentication based on different entropies is compared. In this study, self-face and non-self-face images are used to induce EEG signals for the authentication process. Eventually, the average accuracy of 16 subjects by jackknife test was 90.7%, which demonstrating its better authentication performance and the proposed method achieving higher performance compared with previous methods of EEG-based person authentication. The results also show that, though the four types of entropies were used as the feature extraction methods, the fuzzy entropy achieved the best performance for person authentication.