A group-level stimulus-aware self-supervised soft contrastive learning framework for electroencephalogram emotion recognition_Journal of Biomedical Engineering

Authors：

 CHEN Jingxia , WANG Qian , LI Xiaochi , ZHANG Pengwei

School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an 710021, P. R. China;

Corresponding?author：

CHEN Jingxia, Email: chenjingxia@sust.edu.cn

Keywords：

Emotion recognition; Electroencephalogram signals; Contrastive learning; Soft assignment mechanism; Learnable shuffle splitting

DOI：

10.7507/1001-5515.202504004

Video：

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To reduce the label dependency of traditional electroencephalogram(EEG) emotion recognition methods and address the limitations of existing contrastive learning approaches in modeling cross-stimulus emotional similarity, this paper proposes a group-level stimulus-aware self-supervised soft contrastive learning framework (GSCL) for EEG emotion recognition. GSCL constructs contrastive learning tasks based on the consistency of subjects' brain activities under identical stimuli and incorporates a soft assignment mechanism, which adaptively adjusts the weights of negative sample pairs according to inter-sample distances to enhance representation quality. Additionally, this study also designs a learnable shuffling-splitting data augmentation method to dynamically optimize data distribution via learnable shuffling parameters. Finally, on the public emotional dataset (DEAP), the proposed method achieves accuracies of 94.91%, 95.29%, and 92.78% for valence, arousal, and four-class classification tasks, respectively; while on the Shanghai Jiao Tong University Emotional EEG Dataset (SEED), its three-class classification accuracy reaches 95.25% as well. These results demonstrate that the proposed method yields higher classification accuracy, offering a new insight for self-supervised EEG emotion recognition.

Citation： CHEN Jingxia, WANG Qian, LI Xiaochi, ZHANG Pengwei. A group-level stimulus-aware self-supervised soft contrastive learning framework for electroencephalogram emotion recognition. Journal of Biomedical Engineering, 2026, 43(1): 34-44. doi: 10.7507/1001-5515.202504004 Copy

1.	Wang X, Ren Y, Luo Z, et al. Deep learning-based EEG emotion recognition: current trends and future perspectives. Frontiers in Psychology, 2023, 14: 1126994.
2.	Kamble K, Sengupta J. A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals. Multimedia Tools and Applications, 2023, 82(18): 27269-27304.
3.	張志雯, 于乃功, 邊琰, 等. 基于多模態生理信號特征融合的情緒識別方法研究. 生物醫學工程學雜志, 2025, 42(1): 17-23.
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5.	揭麗琳, 鄒楊萌, 黎政秀, 等. 跨模態特征融合與全局感知的情緒轉換識別方法. 生物醫學工程學雜志, 2025, 42(5): 977-986.
6.	馮國紅, 鄭瀟, 張彬, 等. 基于獨立成分分析—遞歸圖和改進的高效能網絡的EEG情緒識別研究. 生物醫學工程學雜志, 2024, 41(6): 1103-1109.
7.	Wang Y, Zhang B, Di L. Research progress of EEG-based emotion recognition: a survey. ACM Computing Surveys, 2024, 56(11): 1-49.
8.	Cheah K H, Nisar H, Yap V V, et al. Optimizing residual networks and VGG for classification of EEG signals: Identifying ideal channels for emotion recognition. Journal of Healthcare Engineering, 2021, 2021: 5599615.
9.	Tao W, Li C, Song R, et al. EEG-based emotion recognition via channel-wise attention and self attention. IEEE Transactions on Affective Computing, 2020, 14(1): 382-393.
10.	Ding Y, Robinson N, Tong C, et al. LGGNet: learning from local-global-graph representations for brain-computer interface. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(7): 9773-9786.
11.	雪雯, 陳景霞, 胡凱蕾, 等. 基于EEG和面部視頻的多模態連續情感識別. 陜西科技大學學報, 2024, 42(1): 169-176.
12.	Weng W, Gu Y, Guo S, et al. Self-supervised learning for electroencephalogram: a systematic survey. ACM Computing Surveys, 2025, 57(12): 1-38.
13.	陳景霞, 李小池, 王倩, 等. 多自監督學習任務結合圖神經網絡的EEG情感識別. 計算機工程與應用, 2025, 61(22): 205-214.
14.	Zhang K, Wen Q, Zhang C, et al. Self-supervised learning for time series analysis: taxonomy, progress, and prospects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(10): 6775-6794.
15.	Gui J, Chen T, Zhang J, et al. A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 9052-9071.
16.	Liu J, Chen S. TimesURL: self-supervised contrastive learning for universal time series representation learning//Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Vancouver: AAAI Press, 2024, 38(12): 13918-13926.
17.	Alghamdi A M, Ashraf M U, Bahaddad A A, et al. Cross-subject EEG signals-based emotion recognition using contrastive learning. Scientific Reports, 2025, 15(1): 28295.
18.	Bridwell D A, Roth C, Gupta C N, et al. Cortical response similarities predict which audiovisual clips individuals viewed, but are unrelated to clip preference. PLoS One, 2015, 10(6): e0128833.
19.	Dmochowski J P, Bezdek M A, Abelson B P, et al. Audience preferences are predicted by temporal reliability of neural processing. Nature Communications, 2014, 5: 4567.
20.	Shen X, Liu X, Hu X, et al. Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition. IEEE Transactions on Affective Computing, 2022, 14(3): 2496-2511.
21.	Kan H, Yu J, Huang J, et al. Self-supervised group meiosis contrastive learning for EEG-based emotion recognition. Applied Intelligence, 2023, 53(22): 27207-27225.
22.	Dai S, Li M, Wu X, et al. Contrastive learning of EEG representation of brain area for emotion recognition. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 1-13.
23.	Yang Y, Dong X, Qiang Y. CLGSI: a multimodal sentiment analysis framework based on contrastive learning guided by sentiment intensity//Findings of the Association for Computational Linguistics: NAACL 2024, Mexico City: ACL, 2024: 2099-2110.
24.	Lan X, Yan H, Hong S, et al. Towards enhancing time series contrastive learning: a dynamic bad pair mining approach. arXiv preprint, 2023, arXiv: 2302. 03357.
25.	Cai H, Zhang X, Liu X. Semi-supervised end-to-end contrastive learning for time series classification. arXiv preprint, 2023, arXiv: 2310. 08848.
26.	Li X, Song J, Zhao Z, et al. A supervised information enhanced multi-granularity contrastive learning framework for EEG based emotion recognition//2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul: IEEE, 2024: 2325-2329.
27.	Grover S, Jalali A, Etemad A. Segment, shuffle, and stitch: a simple layer for improving time-series representations. Advances in Neural Information Processing Systems, 2024, 37: 4878-4905.
28.	Liu M, Zeng A, Chen M, et al. Scinet: time series modeling and forecasting with sample convolution and interaction. Advances in Neural Information Processing Systems, 2022, 35: 5816-5828.
29.	Feng C, Patras I. Adaptive soft contrastive learning//2022 26th International Conference on Pattern Recognition (ICPR), Montreal: IEEE, 2022: 2721-2727.
30.	Lee S, Park T, Lee K. Soft contrastive learning for time series//12th International Conference on Learning Representations (ICLR), Vienna: ICLR, 2024: 46815-46839.
31.	Yadav M, Alam M A. Dynamic time warping (DTW) algorithm in speech: a review. International Journal of Research in Electronics and Computer Engineering, 2018, 6(1): 524-528.
32.	Koelstra S, Muhl C, Soleymani M, et al. Deap: a database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 2011, 3(1): 18-31.
33.	Zheng W L, Lu B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, 2015, 7(3): 162-175.
34.	Gao Z, Wang X, Yang Y, et al. A channel-fused dense convolutional network for EEG-based emotion recognition. IEEE Transactions on Cognitive and Developmental Systems, 2020, 13(4): 945-954.
35.	Zhang Z, Liu Y, Zhong S. GANSER: a self-supervised data augmentation framework for EEG-based emotion recognition. IEEE Transactions on Affective Computing, 2022, 14(3): 2048-2063.
36.	Li Y, Wang L, Zheng W, et al. A novel bi-hemispheric discrepancy model for EEG emotion recognition. IEEE Transactions on Cognitive and Developmental Systems, 2020, 13(2): 354-367.

1. Wang X, Ren Y, Luo Z, et al. Deep learning-based EEG emotion recognition: current trends and future perspectives. Frontiers in Psychology, 2023, 14: 1126994.
2. Kamble K, Sengupta J. A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals. Multimedia Tools and Applications, 2023, 82(18): 27269-27304.
3. 張志雯, 于乃功, 邊琰, 等. 基于多模態生理信號特征融合的情緒識別方法研究. 生物醫學工程學雜志, 2025, 42(1): 17-23.
4. Kumar A, Kumar A. Human emotion recognition using machine learning techniques based on the physiological signal. Biomedical Signal Processing and Control, 2025, 100: 107039.
5. 揭麗琳, 鄒楊萌, 黎政秀, 等. 跨模態特征融合與全局感知的情緒轉換識別方法. 生物醫學工程學雜志, 2025, 42(5): 977-986.
6. 馮國紅, 鄭瀟, 張彬, 等. 基于獨立成分分析—遞歸圖和改進的高效能網絡的EEG情緒識別研究. 生物醫學工程學雜志, 2024, 41(6): 1103-1109.
7. Wang Y, Zhang B, Di L. Research progress of EEG-based emotion recognition: a survey. ACM Computing Surveys, 2024, 56(11): 1-49.
8. Cheah K H, Nisar H, Yap V V, et al. Optimizing residual networks and VGG for classification of EEG signals: Identifying ideal channels for emotion recognition. Journal of Healthcare Engineering, 2021, 2021: 5599615.
9. Tao W, Li C, Song R, et al. EEG-based emotion recognition via channel-wise attention and self attention. IEEE Transactions on Affective Computing, 2020, 14(1): 382-393.
10. Ding Y, Robinson N, Tong C, et al. LGGNet: learning from local-global-graph representations for brain-computer interface. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(7): 9773-9786.
11. 雪雯, 陳景霞, 胡凱蕾, 等. 基于EEG和面部視頻的多模態連續情感識別. 陜西科技大學學報, 2024, 42(1): 169-176.
12. Weng W, Gu Y, Guo S, et al. Self-supervised learning for electroencephalogram: a systematic survey. ACM Computing Surveys, 2025, 57(12): 1-38.
13. 陳景霞, 李小池, 王倩, 等. 多自監督學習任務結合圖神經網絡的EEG情感識別. 計算機工程與應用, 2025, 61(22): 205-214.
14. Zhang K, Wen Q, Zhang C, et al. Self-supervised learning for time series analysis: taxonomy, progress, and prospects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(10): 6775-6794.
15. Gui J, Chen T, Zhang J, et al. A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12): 9052-9071.
16. Liu J, Chen S. TimesURL: self-supervised contrastive learning for universal time series representation learning//Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Vancouver: AAAI Press, 2024, 38(12): 13918-13926.
17. Alghamdi A M, Ashraf M U, Bahaddad A A, et al. Cross-subject EEG signals-based emotion recognition using contrastive learning. Scientific Reports, 2025, 15(1): 28295.
18. Bridwell D A, Roth C, Gupta C N, et al. Cortical response similarities predict which audiovisual clips individuals viewed, but are unrelated to clip preference. PLoS One, 2015, 10(6): e0128833.
19. Dmochowski J P, Bezdek M A, Abelson B P, et al. Audience preferences are predicted by temporal reliability of neural processing. Nature Communications, 2014, 5: 4567.
20. Shen X, Liu X, Hu X, et al. Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition. IEEE Transactions on Affective Computing, 2022, 14(3): 2496-2511.
21. Kan H, Yu J, Huang J, et al. Self-supervised group meiosis contrastive learning for EEG-based emotion recognition. Applied Intelligence, 2023, 53(22): 27207-27225.
22. Dai S, Li M, Wu X, et al. Contrastive learning of EEG representation of brain area for emotion recognition. IEEE Transactions on Instrumentation and Measurement, 2025, 74: 1-13.
23. Yang Y, Dong X, Qiang Y. CLGSI: a multimodal sentiment analysis framework based on contrastive learning guided by sentiment intensity//Findings of the Association for Computational Linguistics: NAACL 2024, Mexico City: ACL, 2024: 2099-2110.
24. Lan X, Yan H, Hong S, et al. Towards enhancing time series contrastive learning: a dynamic bad pair mining approach. arXiv preprint, 2023, arXiv: 2302. 03357.
25. Cai H, Zhang X, Liu X. Semi-supervised end-to-end contrastive learning for time series classification. arXiv preprint, 2023, arXiv: 2310. 08848.
26. Li X, Song J, Zhao Z, et al. A supervised information enhanced multi-granularity contrastive learning framework for EEG based emotion recognition//2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul: IEEE, 2024: 2325-2329.
27. Grover S, Jalali A, Etemad A. Segment, shuffle, and stitch: a simple layer for improving time-series representations. Advances in Neural Information Processing Systems, 2024, 37: 4878-4905.
28. Liu M, Zeng A, Chen M, et al. Scinet: time series modeling and forecasting with sample convolution and interaction. Advances in Neural Information Processing Systems, 2022, 35: 5816-5828.
29. Feng C, Patras I. Adaptive soft contrastive learning//2022 26th International Conference on Pattern Recognition (ICPR), Montreal: IEEE, 2022: 2721-2727.
30. Lee S, Park T, Lee K. Soft contrastive learning for time series//12th International Conference on Learning Representations (ICLR), Vienna: ICLR, 2024: 46815-46839.
31. Yadav M, Alam M A. Dynamic time warping (DTW) algorithm in speech: a review. International Journal of Research in Electronics and Computer Engineering, 2018, 6(1): 524-528.
32. Koelstra S, Muhl C, Soleymani M, et al. Deap: a database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 2011, 3(1): 18-31.
33. Zheng W L, Lu B L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Transactions on Autonomous Mental Development, 2015, 7(3): 162-175.
34. Gao Z, Wang X, Yang Y, et al. A channel-fused dense convolutional network for EEG-based emotion recognition. IEEE Transactions on Cognitive and Developmental Systems, 2020, 13(4): 945-954.
35. Zhang Z, Liu Y, Zhong S. GANSER: a self-supervised data augmentation framework for EEG-based emotion recognition. IEEE Transactions on Affective Computing, 2022, 14(3): 2048-2063.
36. Li Y, Wang L, Zheng W, et al. A novel bi-hemispheric discrepancy model for EEG emotion recognition. IEEE Transactions on Cognitive and Developmental Systems, 2020, 13(2): 354-367.

Journal of Biomedical Engineering

A group-level stimulus-aware self-supervised soft contrastive learning framework for electroencephalogram emotion recognition

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