DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition

• URL: http://arxiv.org/abs/2510.17475v1
• Date: Mon, 20 Oct 2025 12:18:46 GMT
• Title: DAMSDAN: Distribution-Aware Multi-Source Domain Adaptation Network for Cross-Domain EEG-based Emotion Recognition
• Authors: Fo Hu, Can Wang, Qinxu Zheng, Xusheng Yang, Bin Zhou, Gang Li, Yu Sun, Wen-an Zhang,
• Abstract summary: We propose a distribution-aware multi-source domain adaptation network (DAMSDAN) for emotion recognition.<n>DAMSDAN integrates prototype-based constraints with adversarial learning to drive the encoder toward discriminative, domain-invariant emotion representations.<n>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.
• Score: 19.010493629153288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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