SASD-MCL: Semi-supervised alignment self-distillation with mixed contrastive learning for cross-subject EEG emotion recognition

• Published in: Neural networks Vol. 190; p. 107614
• Main Authors: Zhang, Yong, Chen, Wenyun, Cai, Xingxing, Cheng, Cheng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2025
• Subjects: Algorithms; Electroencephalography (EEG); Electroencephalography - methods; Emotion recognition; Emotions - physiology; Humans; Knowledge distillation; Machine Learning; Multi-domain adaptation; Pattern Recognition, Automated - methods; Semi-supervised learning; Supervised Machine Learning; Emotion recognition; Electroencephalography (EEG); Multi-domain adaptation; Semi-supervised learning; Knowledge distillation
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2025.107614

Electroencephalography (EEG) serves as a valuable technique for objective emotion recognition, with promising applications across diverse fields. However, a major obstacle to the general application of EEG-based emotion recognition systems is the lack of labeled data. To overcome this limitation, we propose a semi-supervised alignment self-knowledge distillation with a mixed contrastive learning model (SASD-MCL) for cross-subject EEG emotion recognition, addressing the issue of limited labeled data. Firstly, we utilize mixed contrastive data augmentation methods to enhance data diversity and richness. Secondly, we introduce semi-supervised similarity alignment techniques to effectively combine labeled and unlabeled data, thereby improving the model’s generalization and robustness. Then, we utilize unsupervised self-knowledge distillation to convey intricate complex knowledge, expediting the adaptation process to the features of the target domain. Finally, we use semi-supervised multi-domain adaptation algorithms to successfully deal with data distribution disparities across various domains (labeled, unlabeled source and target domains), boosting the model’s robustness and performance in cross-subject emotion recognition. Extensive experiments employing a semi-supervised cross-subject leave-one-subject-out validation methodology on the SEED and SEED-IV benchmark datasets demonstrate that our proposed model outperforms existing methods under various imperfect labeling scenarios. The model effectively resolves label scarcity issues in cross-subject emotion recognition using EEG, achieving average performance increases of 5.93% on SEED and 5.32% on SEED-IV.