Decoupled Feature Interaction for Sparse EEG-Based Emotion Recognition

Sparse-channel EEG emotion recognition focuses on selecting specific brain regions or a small number of channels to achieve efficient and robust emotion recognition. Although previous studies have demonstrated excellent performance using dense EEG signals, sparse-channel EEG poses a challenge to recognition performance due to its limited feature representation capability. To address these challenges, we propose a Decoupled Feature Interaction (DFI) method to improve sparse-channel EEG emotion recognition. The proposed method flexibly focuses on decoupled features while enabling adaptive cross-feature information interaction, aiming to enhance the contribution of each feature in sparse EEG data. Specifically, we design a self-supervised auxiliary task that enhances representation learning while generating augmented data. The representations of the original and augmented data are decoupled into two components: invariant features and adaptive features. DFI supervises these decoupled features in a high-dimensional space to maximize their separation. Each decoupled component is dynamically attended to within DFI, with cross-attention applied to adaptive features and self-attention applied to invariant features, enabling both inter- and intra-feature interactions. We evaluate the proposed method on public datasets, and the results consistently demonstrate its superiority over existing emotion recognition methods. To evaluate the model under real-world conditions, we constructed a private dataset containing 3-channel electroencephalogram recordings. On this dataset, DFI achieved an accuracy of 98.58% and an F1 score of 98.92% in binary emotion classification, clearly demonstrating its superiority over existing methods.