<ul><li>Researchers propose a multi-source dynamic contrastive domain adaptation method (MS-DCDA) for EEG emotion recognition.</li><li>The method leverages domain knowledge from multiple sources and uses dynamically weighted learning for optimal tradeoff between domain transferability and discriminability.</li><li>The proposed MS-DCDA model achieves high accuracies in cross-subject and cross-session experiments on SEED and SEED-IV datasets.</li><li>Insights from the study suggest greater emotional sensitivity in frontal and parietal brain lobes, with potential implications for mental health interventions and personalized medicine.</li></ul>

Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation

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