ArticleBioengineering (Basel, Switzerland)2025
Cross-Subject Emotion Recognition with CT-ELCAN: Leveraging Cross-Modal Transformer and Enhanced Learning-Classify Adversarial Network.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Cross-Subject EEG Emotion Recognition Using SSA-EMS Algorithm for Feature Extraction.Entropy (Basel, Switzerland) · 2025Article
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4 authors.
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Abstract
Multimodal physiological emotion recognition is challenged by modality heterogeneity and inter-subject variability, which hinder model generalization and robustness. To address these issues, this paper proposes a new framework, Cross-modal Transformer with Enhanced Learning-Classifying Adversarial Network (CT-ELCAN). The core idea of CT-ELCAN is to shift the focus from conventional signal fusion to the alignment of modality- and subject-invariant emotional representations. By combining a cross-modal Transformer with ELCAN, a feature alignment module using adversarial training, CT-ELCAN learns modality- and subject-invariant emotional representations. Experimental results on the public datasets DEAP and WESAD demonstrate that CT-ELCAN achieves accuracy improvements of approximately 7% and 5%, respectively, compared to state-of-the-art models, while also exhibiting enhanced robustness.
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Registered trials
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