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Deep Representation Learning With Assisted Domain Adaptation for EEG-Based Cross-Subject Emotion Recognition
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- Authors
- Muhammad Zubair, Sungpil Woo, Sunhwan Lim, Daeyoung Kim
- Issue Date
- 2025-11
- Citation
- IEEE Transactions on Computational Social Systems, v.권호미정, pp.1-14
- ISSN
- 2329-924X
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- Emotion recognition based on electroencephalography (EEG) signals has many potential applications. However, the development of an emotion recognition model for real-world deployment encounters significant setbacks in learning an invariant representation due to data distribution shift. Some prior studies adopted domain adaptation methods to tackle this issue. However, these studies perform marginally to tackle distribution shift in the source data and neglect to focus on the extraction of domain and target-specific features, which are crucial for domain invariant representation learning. In this study, we propose an assisted domain adaptive representation learning (ADARL) method that learns domain invariant and emotion-specific features for recognizing emotions across different subjects. The proposed architecture introduces novel attention modules to segregate domain-specific and emotion-specific feature maps by exploiting the inter-relationship between different channels and bands. The domain-specific residual feature maps (peripheral features) extracted by the proposed attention mechanism are used to assist the domain discriminator that forces the model to learn a generalized representation of EEG signals. To tackle the inter-subject variability in the source data, we introduce a novel class-aware compactness loss function to improve the domain adaptation transfer efficiency. The experiments conducted on three EEG datasets (SEED, SEED-IV, and DEAP) demonstrate that the ADARL method significantly improves emotion recognition performance and efficiently alleviates the domain shift problem by learning a generalized representation of EEG signals.
- KSP Keywords
- Attention mechanism, Data Distribution, Deep representation learning, Domain-specific, EEG signals, Emotion Recognition, Feature map, Generalized representation, Inter-subject variability, Potential applications, Real-world deployment