Commonality and individuality based graph learning network for EEG emotion recognition

<i>Objective.</i>The interplay between individual differences and shared human characteristics significantly impacts electroencephalogram (EEG) emotion recognition models, yet remains underexplored. To address this, we propose a commonality and individuality-based EEG graph learning network (CI-Graph), which captures both shared patterns and unique features to improve recognition accuracy.<i>Approach.</i>The proposed model integrates two key components, namely C-Graph and I-Graph, to synthesize a comprehensive graph representation. The C-Graph learns a commonality-based graph applied uniformly to all EEG samples, capturing shared emotional patterns across individuals. The Bootstrap method ensures stable updates while integrating complementary information from the I-Graph. In contrast, the I-Graph dynamically constructs individualized graphs for each sample using a dedicated graph learning module, capturing unique individual features. To enhance representation learning, the model employs a tokenized graph Transformer for robust data encoding and global context modeling, alongside graph diffusion convolution to refine graph connectivity and spatial convolution layer to strengthen local feature extraction. Finally, to reinforce feature learning constraints and accelerate model convergence, we employ a multi-task joint optimization strategy by integrating a self-supervised regression task and a contrastive learning task with the downstream classification task.<i>Main results.</i>We rigorously evaluate our CI-Graph model on three benchmark datasets: SEED, SEED-IV, and DEAP (both Arousal and Valence). Experimental results demonstrate consistent improvements in classification accuracy across all datasets, regardless of the classifier used.<i>Significance.</i>This study demonstrates the critical role of combining signal commonality and individuality in EEG-based emotion recognition. The proposed approach achieves cross-data and cross-model generalization, highlighting its broad applicability and potential to advance the field.