Complete Multi-Domain Decoupled Fusion Model for EEG-Based Person Identification

Abstract

Electroencephalogram (EEG) signals have unique individual characteristics and have broad application prospects in identity authentication. At present, person identification (PI) based on EEG using the temporal-spatial-spectral feature extraction framework has achieved remarkable success. However, the existing methods suffer from coupled cross-domain feature parameters and insufficient feature fusion during feature extraction, which limits the recognition ability. Moreover, fixed-scale feature extractors can hardly exploit the subject-specific multi-scale information. To address these challenges, we propose CMDFM: a complete multi-domain decoupled fusion model for EEG-based PI. Firstly, we design an independent temporal-spatial-spectral attention mechanism to eliminate cross-domain parameter coupling. Secondly, a full-domain fusion mechanism is designed to comprehensively integrate the features of the temporal domain, spatial domain and spectral domain. Finally, an adaptive multi-scale CNN is designed to adjust the contribution of the multi-scale convolution kernel, thereby making full use of individual-specific multi-scale information. We use four datasets to verify our method. The experimental results show that our method is superior to all the state-of-the-art methods. The code of CMDFM is at https://github.com/2538441690/CMDFM.