  Person identification technology recognizes individuals by exploiting their
unique, measurable physiological and behavioral characteristics. However, the
state-of-the-art person identification systems have been shown to be
vulnerable, e.g., contact lenses can trick iris recognition and fingerprint
films can deceive fingerprint sensors. EEG (Electroencephalography)-based
identification, which utilizes the users' brainwave signals for identification
and offers a more resilient solution, draw a lot of attention recently.
However, the accuracy still requires improvement and very little work is
focusing on the robustness and adaptability of the identification system. We
propose MindID, an EEG-based biometric identification approach, achieves higher
accuracy and better characteristics. At first, the EEG data patterns are
analyzed and the results show that the Delta pattern contains the most
distinctive information for user identification. Then the decomposed Delta
pattern is fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural
Networks) structure which assigns various attention weights to different EEG
channels based on the importance of channels. The discriminative
representations learned from the attention-based RNN are used to recognize the
user identification through a boosting classifier. The proposed approach is
evaluated over 3 datasets (two local and one public). One local dataset (EID-M)
is used for performance assessment and the result illustrates that our model
achieves an accuracy of 0.982 which outperforms the baselines and the
state-of-the-art. Another local dataset (EID-S) and a public dataset (EEG-S)
are utilized to demonstrate the robustness and adaptability, respectively. The
results indicate that the proposed approach has the potential to be largely
deployed in the practice environment.
