MS²FL: Modality-shared and Modality-specific Feature Learning for Multimodal Emotion Recognition
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Le résumé fourni par la source
Multimodal emotion recognition based on complementary physiological signals such as electroencephalogram (EEG) and eye movements can effectively reflect human emotional states, demonstrating significant potential in fields such as rehabilitation monitoring and driving safety. However, existing multimodal emotion recognition approaches often focus solely on either feature fusion or feature alignment strategies, lacking a unified framework capturing both complementary and modality-specific representations. To address this limitation, we propose a modality-shared and modality-specific feature learning framework (MS \({}^{2}\) FL) for multimodal emotion recognition. Specifically, a dual-path encoder is employed first to extract the spatiotemporal frequency features of EEG signals and dynamic response features of eye movement signals, respectively. Then, a specificity-preserving feature distribution alignment mechanism is introduced to alleviate distribution discrepancies between modalities while reinforcing modality-specific modeling of distinctive features. Finally, a feature distribution enhancement fusion strategy is utilized to effectively integrate the shared features across modalities, thus improving the representational capacity of the fused features. Experimental results demonstrate that MS \({}^{2}\) FL achieves recognition accuracies of 96.99% and 89.83% on the SEED and SEED-V datasets, respectively, significantly improving emotion recognition performance. This study provides a concise and effective solution for emotion perception neurotechnology and offers a practical solution for emotion perception health monitoring and rehabilitation.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- MS²FL: Modality-shared and Modality-specific Feature Learning for Multimodal Emotion Recognition
- Date Crossref
- 19/08/2026
- Éditeur
- Association for Computing Machinery (ACM)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Anhui University pays non établi dans la noticeUniversité ou école supérieure
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Hefei Normal University pays non établi dans la noticeUniversité ou école supérieure
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Civil Aviation Flight University of China CAAC Key Laboratory of Civil Aviation Flight Technology and Flight Safety pays non établi dans la noticeUniversité ou école supérieure
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Hefei University of Technology pays non établi dans la noticeUniversité ou école supérieure
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University of Science and Technology of China Institute of Advanced Technology pays non établi dans la noticeUniversité ou école supérieure
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School of Computer Science and Technology Anhui Province Key Laboratory of Multimodal Cognitive Computation pays non établi dans la noticeUniversité ou école supérieure
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School of Computer Science and Artificial Intelligence pays non établi dans la noticeUniversité ou école supérieure
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School of Computer Science and Information Engineering pays non établi dans la noticeUniversité ou école supérieure
Anhui University, Hefei Normal University et CAAC Key Laboratory of Civil Aviation Flight Technology and Flight Safety — Civil Aviation Flight University of China, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.