Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation
This work addresses the problem of accurate emotion recognition from EEG for applications in mental health interventions, personalized medicine, and preventive strategies, though it is incremental as it builds on existing domain adaptation methods.
The paper tackled emotion recognition from EEG signals by introducing a multi-source dynamic contrastive domain adaptation method (MS-DCDA) to address signal variations across individuals and sessions, achieving the highest mean accuracies of 90.84% and 78.49% on SEED and SEED-IV datasets in cross-subject experiments, and 95.82% and 82.25% in cross-session experiments.
Electroencephalography (EEG) provides reliable indications of human cognition and mental states. Accurate emotion recognition from EEG remains challenging due to signal variations among individuals and across measurement sessions. We introduce a multi-source dynamic contrastive domain adaptation method (MS-DCDA) based on differential entropy (DE) features, in which coarse-grained inter-domain and fine-grained intra-class adaptations are modeled through a multi-branch contrastive neural network and contrastive sub-domain discrepancy learning. Leveraging domain knowledge from each individual source and a complementary source ensemble, our model uses dynamically weighted learning to achieve an optimal tradeoff between domain transferability and discriminability. The proposed MS-DCDA model was evaluated using the SEED and SEED-IV datasets, achieving respectively the highest mean accuracies of $90.84\%$ and $78.49\%$ in cross-subject experiments as well as $95.82\%$ and $82.25\%$ in cross-session experiments. Our model outperforms several alternative domain adaptation methods in recognition accuracy, inter-class margin, and intra-class compactness. Our study also suggests greater emotional sensitivity in the frontal and parietal brain lobes, providing insights for mental health interventions, personalized medicine, and preventive strategies.