Significant inter-individual variability limits the generalization of EEG-based emotion recognition under cross-domain settings. We address two core challenges in multi-source adaptation: (1) dynamically modeling distributional heterogeneity across sources and quantifying their relevance to a target to reduce negative transfer; and (2) achieving fine-grained semantic consistency to strengthen class discrimination. We propose a distribution-aware multi-source domain adaptation network (DAMSDAN). DAMSDAN integrates prototype-based constraints with adversarial learning to drive the encoder toward discriminative, domain-invariant emotion representations. A domain-aware source weighting strategy based on maximum mean discrepancy (MMD) dynamically estimates inter-domain shifts and reweights source contributions. In addition, a prototype-guided conditional alignment module with dual pseudo-label interaction enhances pseudo-label reliability and enables category-level, fine-grained alignment, mitigating noise propagation and semantic drift. Experiments on SEED and SEED-IV show average accuracies of 94.86\% and 79.78\% for cross-subject, and 95.12\% and 83.15\% for cross-session protocols. On the large-scale FACED dataset, DAMSDAN achieves 82.88\% (cross-subject). Extensive ablations and interpretability analyses corroborate the effectiveness of the proposed framework for cross-domain EEG-based emotion recognition.

翻译：显著的个体间差异性限制了基于脑电的情绪识别在跨域设置下的泛化能力。我们针对多源自适应中的两个核心挑战：（1）动态建模源域间的分布异质性并量化其与目标域的相关性，以减少负迁移；（2）实现细粒度的语义一致性以增强类别区分度。我们提出了一种分布感知多源域自适应网络（DAMSDAN）。DAMSDAN将基于原型的约束与对抗学习相结合，以驱动编码器学习具有判别性且域不变的情绪表征。一种基于最大均值差异（MMD）的域感知源加权策略动态估计域间偏移并重新加权各源域的贡献。此外，一个具有双重伪标签交互的原型引导条件对齐模块增强了伪标签的可靠性，并实现了类别级的细粒度对齐，从而缓解了噪声传播和语义漂移。在SEED和SEED-IV数据集上的实验表明，在跨被试协议下平均准确率分别为94.86%和79.78%，在跨会话协议下分别为95.12%和83.15%。在大规模FACED数据集上，DAMSDAN在跨被试协议下达到了82.88%的准确率。广泛的消融实验和可解释性分析证实了所提框架在跨域脑电情绪识别中的有效性。