Personal Identification and Authentication in Multi-Task EEG Database Using EEGNet and Siamese Network

Currently, there is a growing global concern regarding data privacy and security, particularly in the field of personal identification and authentication. Traditional biometric identification technologies are highly favored for their ease of use and high accuracy, but they fall short in ensuring liveness detection, making them susceptible to deception and forgery threats. This study focuses on personal identification and authentication based on a multi-task electroencephalogram (EEG) database, proposing an innovative model framework for these purposes. To validate the effectiveness of this model, we established a multitask EEG database containing data from 24 subjects engaged in five mental tasks. Each subject underwent four sessions, with each session consisting of 125 trials, and session intervals ranging from days to months. In this framework, we employed the EEGNet model for personal identification. It directly utilized preprocessed EEG data as input, mapping input signals to a new embedding space to extract identity features, ultimately achieving accurate individual personal identification. For the personal authentication module, we proposed the SiamEEGNet model, combining concepts from EEGNet and Siamese networks. This model comprised two EEGNet sub-networks with identical model parameters. Unlike the personal identification module, we removed the classification module from the EEGNet model in the SiamEEGNet model. Instead, we introduced a distance measurement layer to calculate the distance or similarity between the outputs of the two sub-networks, thereby achieving personal authentication. We conducted experiments for both personal identification and authentication. In the identification experiments, the proposed EEGNet model demonstrated outstanding performance with an average recognition accuracy of 99.84%. The model achieved a balance between precision and sensitivity, as reflected in high F1 score values. In the personal authentication experiments, the new SiamEEGNet model achieved a False Rejection Rate (FRR) of 2.29% and a False Acceptance Rate (FAR) of 4.75%. The experimental results collectively indicate the significant effectiveness of the proposed model framework.