Affective EEG-Based Person Identification With Continual Learning

Electroencephalograms (EEGs) have garnered immense attention due to their security features, which are difficult to physically counterfeit in the field of person identification. Despite significant achievements in EEG-based person identification, several challenges remain: 1) how to dynamically update the model to identify an increasing number of users; 2) how to more effectively capture and reconstruct the interrelationships of features from different domains in EEG signals; and 3) how to enhance the core capabilities of the backbone network, including the acquisition of global features while mining fine-grained local features. To address these challenges, this article proposes an affective EEG-based person identification with continual learning, enabling the model to dynamically adapt to the escalating needs of user identification. Furthermore, we developed a multidomain coordinated attention transformer (MD-CAT) to serve as the backbone network. This backbone network combines spatial and time-frequency attention mechanisms with domain coordinated mechanisms, enabling it to adaptively capture fine-grained local features and reconstruct interactions across domains on a macro level. We validated our proposed method using the THU-EP dataset, a nine-class affective state (AS) dataset involving 80 subjects. Experimental results demonstrate that our method surpasses current advanced benchmarks. In addition, we analyzed the impact of different ASs and frequency bands (FBs) on affective EEG-based person identification and found that the neutral state and beta band had minimal impacts on the decay of accuracy in continual learning. The code is publicly available at
https://github.com/JerryKingQAQ/
AEEG-PI-CL.