Prototypical Contrastive Learning With Temporal Dynamic Graph Convolutional Network for EEG-Based Emotion Recognition

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Yi Yang, Chenxi Sun, Ruoning Lyu, Jiaqi Wang, Ze Wang, Xun Chen, Chin-Teng Lin, Tzyy-Ping Jung, Feng Wan

Published: 01 Jan 2026, Last Modified: 14 Apr 2026IEEE Transactions on Affective ComputingEveryoneRevisionsCC BY-SA 4.0
Abstract: Electroencephalogram (EEG) signals are inherently non-stationary and exhibit significant inter-subject variability, leading to pronounced cross-subject distribution shifts that hinder accurate emotion recognition. Although graph convolutional networks (GCNs) and domain adaptation (DA) methods have made progress in mitigating individual differences, existing approaches still face two fundamental limitations: (1) traditional GCNs rely on static functional connectivity graphs, which fail to capture the dynamic temporal evolution of neural interactions during emotional processes, and (2) most DA-based methods only emphasize global feature alignment while overlooking emotion-specific semantic structures, thereby impairing both fine-grained discriminability and cross-subject generalization. To overcome these challenges, we propose the Prototypical Contrastive Learning with Temporal Dynamic Graph Convolutional Network (PCL-TDGCN) for EEG-based emotion recognition. Specifically, we construct an adaptive global EEG pattern memory mechanism to model temporally dynamic brain networks, thereby facilitating spatiotemporal neural interactions essential for emotion representation learning. Furthermore, we design a prototypical contrastive learning strategy that incorporates: (i) intra-domain contrastive learning to enhance the discriminability of emotional state representations, and (ii) inter-domain contrastive learning to mitigate distribution shifts across domains via semantic-aware prototypical alignment. Extensive experiments on three public datasets demonstrate that the proposed PCL-TDGCN outperforms state-of-the-art methods, achieving accuracy improvements of 1.08% (SEED), 6.53% (HIED), and 0.98% (SEED-IV) in subject-dependent experiments, and 1.08% (SEED), 7.51% (HIED), and 1.99% (SEED-IV) in subject-independent scenarios, respectively.