Multi-source adversarial adaptation with calibration for electroencephalogram-based classification of meditation and resting states_Journal of Biomedical Engineering

Authors：

GOU Mingyu ¹ , YIN Haolong ² , CHEN Tianzhen ³ , CHENG Fei ³ ,  DU Jiang ² , LYU Baoliang ² ,  ZHENG Weilong ²

1. Paris Elite Institute of Technology, Shanghai Jiao Tong University, Shanghai 200240, P. R. China;
2. School of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, P. R. China;
3. Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, P. R. China;

Corresponding author：

DU Jiang, Email: dujiangdou@163.com; ZHENG Weilong, Email: weilong@sjtu.edu.cn

Keywords：

Electroencephalogram; Meditation; Model integration; Multi-source domain adaptation

DOI：

10.7507/1001-5515.202504044

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model—calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.

Citation： GOU Mingyu, YIN Haolong, CHEN Tianzhen, CHENG Fei, DU Jiang, LYU Baoliang, ZHENG Weilong. Multi-source adversarial adaptation with calibration for electroencephalogram-based classification of meditation and resting states. Journal of Biomedical Engineering, 2025, 42(4): 668-677. doi: 10.7507/1001-5515.202504044 Copy

1.	Tseng H W, Chou F H, Chen C H, et al. Effects of mindfulness-based cognitive therapy on major depressive disorder with multiple episodes: a systematic review and meta-analysis. Int J Environ Res Public Health, 2023, 20(2): 1555.
2.	Francis S E B, Shawyer F, Cayoun B A, et al. Differentiating mindfulness-integrated cognitive behavior therapy and mindfulness-based cognitive therapy clinically: the why, how, and what of evidence-based practice. Front Psychol, 2024, 15: 1342592.
3.	Tsang E W, Gao J, Lo C N, et al. Effects of mindfulness meditation on human impulsivity: a systematic review and meta-analysis. Acad Ment Health Well-Being, 2025, 2(1): 1-24.
4.	Garland E L, Hanley A W, Nakamura Y, et al. Mindfulness-oriented recovery enhancement vs supportive group therapy for co-occurring opioid misuse and chronic pain in primary care: a randomized clinical trial. JAMA Intern Med, 2022, 182(4): 407-417.
5.	Brown K W, Ryan R M. Mindful attention awareness scale. J Pers Soc Psychol, 2003.
6.	Liang Xi Xi, Wang Jiu Ju, Wu Dan, et al. Comparative analysis of brainwave music translated from spontaneous EEG between major depression disorders and healthy people. Brain-Apparatus Commun, 2022, 1(1): 107-125.
7.	Qi G, Liu R, Guan W, et al. Augmented recognition of distracted driving state based on electrophysiological analysis of brain network. Cyborg Bionic Syst, 2024, 5: 0130.
8.	Shi Y, Ji M, Zhong F, et al. Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia. Brain-Apparatus Commun, 2024, 3(1): 2388106.
9.	Lomas T, Ivtzan I, Fu C H Y. A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neurosci Biobehav Rev, 2015, 57: 401-410.
10.	Ye W, Wang J, Chen L, et al. Adaptive spatial–temporal aware graph learning for EEG-based emotion recognition. Cyborg Bionic Syst, 2024, 5: 0088.
11.	Li J, Li J, Wang X, et al. A domain generalization and residual network-based emotion recognition from physiological signals. Cyborg Bionic Syst, 2024, 5: 0074.
12.	Li J, Yu Z, Du Z, et al. A comprehensive survey on source-free domain adaptation. IEEE Trans Pattern Anal Mach Intell, 2024, 46(8): 5743-5762.
13.	Yang J, Yan R, Hauptmann A G. Cross-domain video concept detection using adaptive SVMs// Proceedings of the 15th ACM International Conference on Multimedia. Augsburg: ACM, 2007: 188-197.
14.	Zhu Y, Zhuang F, Wang D. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources// Proceedings of the AAAI Conference on Artificial Intelligence. Honolulu: AAAI, 2019: 5989-5996.
15.	Wang L, Zhang X, Su H, et al. A comprehensive survey of continual learning: Theory, method and application. IEEE Trans Pattern Anal Mach Intell, 2024, 46(8): 5362-5383.
16.	Wang Y, Huang Z, Hong X. S-Prompts learning with pre-trained transformers: An Occam’s Razor for domain incremental learning// Advances in Neural Information Processing Systems 35 (NeurIPS 2022). New Orleans: NeurIPS Foundation, 2022: 5682-5695.
17.	Madl T. Exploring neural markers of dereification in meditation based on EEG and personalized models of electrophysiological brain states. Sci Rep, 2024, 14(1): 24264.
18.	Chen Z, Xia J, Li J, et al. Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network. Adv Eng Inform, 2023, 57: 102033.
19.	Sicilia A, Zhao X, Hwang S J. Domain adversarial neural networks for domain generalization: When it works and how to improve. Mach Learn, 2023, 112(7): 2685-2721.
20.	Zhou F, Chen H. Cross-modal translation and alignment for survival analysis// Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Paris: IEEE/Computer Vision Foundation, 2023: 21485-21494.
21.	Mao A, Mohri M, Zhong Y. Cross-entropy loss functions: Theoretical analysis and applications// Proceedings of the 40th International Conference on Machine Learning. Honolulu: International Machine Learning Society, 2023: 23803-23828.
22.	Nishabh K, Kshitija A N. A systematic review on clinical research and approval process. Asian J Pharm Res Dev, 2023, 11(1): 39-51.
23.	Gorgoni M, Salfi F, De Gennaro L, et al. Electroencephalographic and neurophysiological changes// Kerkhof G A, Van Dongen H P A. Encyclopedia of sleep and circadian rhythms. Second Edition. Oxford: Elsevier, Academic Press, 2023, 1: 313-322.
24.	Gou M, Zhang Y J, Dai R J, et al. Addressing temporal and auditory factors in meditative EEG with self-supervised learning// 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Lisbon: IEEE, 2024: 1954-1959.
25.	Gou M, Yin H L, Lu B L, et al. Multi-modal adversarial regressive transformer for cross-subject fatigue detection// 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando: IEEE, 2024: 1-4.
26.	Liu G, Wen Y, Hsiao J H, et al. EEG-based familiar and unfamiliar face classification using filter-bank differential entropy features. IEEE Trans Hum Mach Syst, 2023, 54(1): 44-55.
27.	Wong K, Dornberger R, Hanne T. An analysis of weight initialization methods in connection with different activation functions for feedforward neural networks. Evol Intell, 2024, 17(3): 2081-2089.
28.	Han S, Qubo C, Meng H. Parameter selection in SVM with RBF kernel function// World Automation Congress 2012. Puerto Vallarta: IEEE, 2012: 1-4.
29.	Abibullaev B, Keutayeva A, Zollanvari A. Deep learning in EEG-based BCIs: a comprehensive review of transformer models, advantages, challenges, and applications. IEEE Access, 2023, 11: 127271-127301.
30.	Liao B, Tan S, Monz C. Make pre-trained model reversible: From parameter to memory efficient fine-tuning// Advances in Neural Information Processing Systems 36 (NeurIPS 2023). New Orleans: NeurIPS Foundation, 2023: 15186-15209.
31.	Qian Q, Wang Y, Zhang T, et al. Maximum mean square discrepancy: a new discrepancy representation metric for mechanical fault transfer diagnosis. Knowl Based Syst, 2023, 276: 110748.
32.	Meyes R, Lu M, Waubert de Puiseau C W, et al. Ablation studies in artificial neural networks. arXiv, 2019: 1901.08644.
33.	Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks// Proceedings of the 34th International Conference on Machine Learning. Sydney: International Machine Learning Society, 2017: 3319-3328.
34.	Sivaranjani S, Haldo J, Rithick R, et al. Analyzing EEG patterns as predictors of physiological responses// 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). Chennai: IEEE, 2024: 1-6.
35.	Holas P, Kamińska J. Mindfulness meditation and psychedelics: potential synergies and commonalities. Pharmacol Rep, 2023, 75(6): 1398-1409.

1. Tseng H W, Chou F H, Chen C H, et al. Effects of mindfulness-based cognitive therapy on major depressive disorder with multiple episodes: a systematic review and meta-analysis. Int J Environ Res Public Health, 2023, 20(2): 1555.
2. Francis S E B, Shawyer F, Cayoun B A, et al. Differentiating mindfulness-integrated cognitive behavior therapy and mindfulness-based cognitive therapy clinically: the why, how, and what of evidence-based practice. Front Psychol, 2024, 15: 1342592.
3. Tsang E W, Gao J, Lo C N, et al. Effects of mindfulness meditation on human impulsivity: a systematic review and meta-analysis. Acad Ment Health Well-Being, 2025, 2(1): 1-24.
4. Garland E L, Hanley A W, Nakamura Y, et al. Mindfulness-oriented recovery enhancement vs supportive group therapy for co-occurring opioid misuse and chronic pain in primary care: a randomized clinical trial. JAMA Intern Med, 2022, 182(4): 407-417.
5. Brown K W, Ryan R M. Mindful attention awareness scale. J Pers Soc Psychol, 2003.
6. Liang Xi Xi, Wang Jiu Ju, Wu Dan, et al. Comparative analysis of brainwave music translated from spontaneous EEG between major depression disorders and healthy people. Brain-Apparatus Commun, 2022, 1(1): 107-125.
7. Qi G, Liu R, Guan W, et al. Augmented recognition of distracted driving state based on electrophysiological analysis of brain network. Cyborg Bionic Syst, 2024, 5: 0130.
8. Shi Y, Ji M, Zhong F, et al. Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia. Brain-Apparatus Commun, 2024, 3(1): 2388106.
9. Lomas T, Ivtzan I, Fu C H Y. A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neurosci Biobehav Rev, 2015, 57: 401-410.
10. Ye W, Wang J, Chen L, et al. Adaptive spatial–temporal aware graph learning for EEG-based emotion recognition. Cyborg Bionic Syst, 2024, 5: 0088.
11. Li J, Li J, Wang X, et al. A domain generalization and residual network-based emotion recognition from physiological signals. Cyborg Bionic Syst, 2024, 5: 0074.
12. Li J, Yu Z, Du Z, et al. A comprehensive survey on source-free domain adaptation. IEEE Trans Pattern Anal Mach Intell, 2024, 46(8): 5743-5762.
13. Yang J, Yan R, Hauptmann A G. Cross-domain video concept detection using adaptive SVMs// Proceedings of the 15th ACM International Conference on Multimedia. Augsburg: ACM, 2007: 188-197.
14. Zhu Y, Zhuang F, Wang D. Aligning domain-specific distribution and classifier for cross-domain classification from multiple sources// Proceedings of the AAAI Conference on Artificial Intelligence. Honolulu: AAAI, 2019: 5989-5996.
15. Wang L, Zhang X, Su H, et al. A comprehensive survey of continual learning: Theory, method and application. IEEE Trans Pattern Anal Mach Intell, 2024, 46(8): 5362-5383.
16. Wang Y, Huang Z, Hong X. S-Prompts learning with pre-trained transformers: An Occam’s Razor for domain incremental learning// Advances in Neural Information Processing Systems 35 (NeurIPS 2022). New Orleans: NeurIPS Foundation, 2022: 5682-5695.
17. Madl T. Exploring neural markers of dereification in meditation based on EEG and personalized models of electrophysiological brain states. Sci Rep, 2024, 14(1): 24264.
18. Chen Z, Xia J, Li J, et al. Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network. Adv Eng Inform, 2023, 57: 102033.
19. Sicilia A, Zhao X, Hwang S J. Domain adversarial neural networks for domain generalization: When it works and how to improve. Mach Learn, 2023, 112(7): 2685-2721.
20. Zhou F, Chen H. Cross-modal translation and alignment for survival analysis// Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Paris: IEEE/Computer Vision Foundation, 2023: 21485-21494.
21. Mao A, Mohri M, Zhong Y. Cross-entropy loss functions: Theoretical analysis and applications// Proceedings of the 40th International Conference on Machine Learning. Honolulu: International Machine Learning Society, 2023: 23803-23828.
22. Nishabh K, Kshitija A N. A systematic review on clinical research and approval process. Asian J Pharm Res Dev, 2023, 11(1): 39-51.
23. Gorgoni M, Salfi F, De Gennaro L, et al. Electroencephalographic and neurophysiological changes// Kerkhof G A, Van Dongen H P A. Encyclopedia of sleep and circadian rhythms. Second Edition. Oxford: Elsevier, Academic Press, 2023, 1: 313-322.
24. Gou M, Zhang Y J, Dai R J, et al. Addressing temporal and auditory factors in meditative EEG with self-supervised learning// 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Lisbon: IEEE, 2024: 1954-1959.
25. Gou M, Yin H L, Lu B L, et al. Multi-modal adversarial regressive transformer for cross-subject fatigue detection// 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Orlando: IEEE, 2024: 1-4.
26. Liu G, Wen Y, Hsiao J H, et al. EEG-based familiar and unfamiliar face classification using filter-bank differential entropy features. IEEE Trans Hum Mach Syst, 2023, 54(1): 44-55.
27. Wong K, Dornberger R, Hanne T. An analysis of weight initialization methods in connection with different activation functions for feedforward neural networks. Evol Intell, 2024, 17(3): 2081-2089.
28. Han S, Qubo C, Meng H. Parameter selection in SVM with RBF kernel function// World Automation Congress 2012. Puerto Vallarta: IEEE, 2012: 1-4.
29. Abibullaev B, Keutayeva A, Zollanvari A. Deep learning in EEG-based BCIs: a comprehensive review of transformer models, advantages, challenges, and applications. IEEE Access, 2023, 11: 127271-127301.
30. Liao B, Tan S, Monz C. Make pre-trained model reversible: From parameter to memory efficient fine-tuning// Advances in Neural Information Processing Systems 36 (NeurIPS 2023). New Orleans: NeurIPS Foundation, 2023: 15186-15209.
31. Qian Q, Wang Y, Zhang T, et al. Maximum mean square discrepancy: a new discrepancy representation metric for mechanical fault transfer diagnosis. Knowl Based Syst, 2023, 276: 110748.
32. Meyes R, Lu M, Waubert de Puiseau C W, et al. Ablation studies in artificial neural networks. arXiv, 2019: 1901.08644.
33. Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks// Proceedings of the 34th International Conference on Machine Learning. Sydney: International Machine Learning Society, 2017: 3319-3328.
34. Sivaranjani S, Haldo J, Rithick R, et al. Analyzing EEG patterns as predictors of physiological responses// 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). Chennai: IEEE, 2024: 1-6.
35. Holas P, Kamińska J. Mindfulness meditation and psychedelics: potential synergies and commonalities. Pharmacol Rep, 2023, 75(6): 1398-1409.

Journal of Biomedical Engineering

Multi-source adversarial adaptation with calibration for electroencephalogram-based classification of meditation and resting states

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content