MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network

TL;DR

MindID introduces an EEG-based person identification system using attention-based RNNs, achieving high accuracy and robustness by focusing on distinctive brainwave patterns, especially the Delta band.

Contribution

The paper presents a novel EEG biometric identification method employing attention-based RNNs, improving accuracy and robustness over existing systems.

Findings

Achieved 98.2% accuracy on local dataset

Outperformed baseline and state-of-the-art methods

Demonstrated robustness and adaptability across datasets

Abstract

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.