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.

翻译：脑电图（EEG）为人类认知与心理状态提供了可靠的指示。由于个体间及测量会话间的信号差异，基于EEG的精准情绪识别仍具挑战性。我们提出了一种基于差分熵（DE）特征的多源动态对比域自适应方法（MS-DCDA），该方法通过多分支对比神经网络与对比子域差异学习，实现了粗粒度的域间适应与细粒度的类内适应。通过利用来自每个独立源域以及互补源域集合的领域知识，我们的模型采用动态加权学习，在域可迁移性与判别性之间达成最优权衡。所提出的MS-DCDA模型在SEED和SEED-IV数据集上进行了评估，在跨被试实验中分别取得了$90.84\%$和$78.49\%$的最高平均准确率，在跨会话实验中分别取得了$95.82\%$和$82.25\%$的最高平均准确率。我们的模型在识别准确率、类间间隔与类内紧密度方面均优于多种替代域自适应方法。我们的研究还表明，额叶与顶叶脑区具有更高的情绪敏感性，这为心理健康干预、个性化医疗及预防策略提供了见解。