A novel mental fatigue detecting method based on single-channel electroencephalogram using hybrid convolutional neural network combined with Transformer_Journal of Biomedical Engineering

Authors：

ZHU Hongyu , ZHEN Qingkai , CHEN Qi ,  YU Tao

China Institute of Sport Science, Beijing 100061, P. R. China;

Corresponding?author：

YU Tao, Email: yutao@ciss.cn

Keywords：

Mental fatigue; Convolutional neural network; Electroencephalogram; Transformer

DOI：

10.7507/1001-5515.202506062

Video：

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Mental fatigue, a detrimental psychophysiological state induced by high-intensity cognitive tasks, impairs athletes’ attention, reaction, and decision-making, increasing the risk of errors and injuries. Traditional questionnaire-based assessments of mental fatigue suffer from subjectivity and response bias, whereas objective examining and analyzing methods such as electroencephalography (EEG) are often costly and time-consuming, highlighting the need for efficient and convenient objective approaches. This study proposes a hybrid convolutional neural network (CNN)–Transformer model that combines CNN-based feature extraction with Transformer-based global dependency modeling for accurate and efficient mental fatigue recognition. The model was evaluated on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED) and the Sustained-Attention Driving Task (SADT) dataset. The model proposed in this study achieved accuracies of 78.07% and 85.42%, respectively, outperforming conventional methods and demonstrating good cross-subject generalization. Furthermore, channel analysis highlighted the occipital regions’ signal as key contributors to fatigue detection, providing theoretical basis for the development of portable and lightweight device for mental fatigue monitoring. Overall, this work provides a feasible solution for efficient and objective mental fatigue detection, and has potential applications in athletic training monitoring and performance optimization.

Citation： ZHU Hongyu, ZHEN Qingkai, CHEN Qi, YU Tao. A novel mental fatigue detecting method based on single-channel electroencephalogram using hybrid convolutional neural network combined with Transformer. Journal of Biomedical Engineering, 2026, 43(1): 53-60. doi: 10.7507/1001-5515.202506062 Copy

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8.	Liu Y, Lan Z, Cui J, et al. Inter-subject transfer learning for EEG-based mental fatigue recognition. Adv Eng Inform, 2020, 46(4): 101157.
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11.	楊慧舟, 劉云飛, 夏麗娟. 前額單通道腦電信號的疲勞特征提取及分類算法. 生物醫學工程學雜志, 2024, 41(4): 732-741.
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15.	Li G, Huang S, Xu W, et al. The impact of mental fatigue on brain activity: a comparative study both in resting state and task state using EEG. BMC Neurosci, 2020, 21(1): 20.
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20.	Cao Z, Chuang C H, King J K, et al. Multi-channel EEG recordings during a sustained-attention driving task. Sci Data, 2019, 6(1): 19.
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22.	Gao X Y, Zhang Y F, Zheng W L, et al. Evaluating driving fatigue detection algorithms using eye tracking glasses//2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), Montpellier: IEEE, 2015: 767-770.
23.	Cui J, Lan Z, Liu Y, et al. A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG. Methods, 2022, 202: 173-184.
24.	Kramer O. K-nearest neighbors//Kramer O. Dimensionality reduction with unsupervised nearest neighbors. Berlin: Springer Berlin Heidelberg, 2013: 13-23.
25.	Cutler A, Cutler D R, Stevens J R. Random forests//Zhang C, Ma Y. Ensemble machine learning: methods and applications. New York: Springer, 2012: 157-175.
26.	Chen S, Webb G I, Liu L, et al. A novel selective na?ve bayes algorithm. Knowl Based Syst, 2020, 192: 105361.
27.	Lederer J. Support-vector machines//Lederer J. A first course in statistical learning: with data examples and python code. Cham: Springer Nature Switzerland, 2025: 187-243.
28.	Alhagry S, Aly A, El-khoribi R A. Emotion recognition based on EEG using LSTM recurrent neural network. Int J Adv Comput Sci Appl, 2017, 8(10): 355-358.
29.	Eldele E, Chen Z, Liu C, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 809-818.
30.	Song Y, Zheng Q, Liu B, et al. EEG Conformer: convolutional Transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 710-719.
31.	Gong P, Wang P, Zhou Y, et al. TFAC-net: a temporal-frequential attentional convolutional network for driver drowsiness recognition with single-channel EEG. IEEE Trans Intell Transp Syst, 2024, 25(7): 7004-7016.
32.	Ma L, Marshall P J, Wright W G. The impact of external and internal focus of attention on visual dependence and EEG alpha oscillations during postural control. J Neuroeng Rehabil, 2022, 19: 81.

1. Habay J, Van Cutsem J, Verschueren J, et al. Mental fatigue and sport-specific psychomotor performance: a systematic review. Sports Med, 2021, 51(7): 1527-1548.
2. Brown D M Y, Graham J D, Innes K I, et al. Effects of prior cognitive exertion on physical performance: a systematic review and meta-analysis. Sports Med, 2020, 50(3): 497-529.
3. Martin K, Meeusen R, Thompson K G, et al. Mental fatigue impairs endurance performance: a physiological explanation. Sports Med, 2018, 48(9): 2041-2051.
4. Kunasegaran K, Ismail A M H, Ramasamy S, et al. Understanding mental fatigue and its detection: a comparative analysis of assessments and tools. PeerJ, 2023, 11: e15744.
5. Holgado D, Mesquida C, Román-caballero R. Assessing the evidential value of mental fatigue and exercise research. Sports Med, 2023, 53(12): 2293-2307.
6. Bini S A. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?. J Arthroplasty, 2018, 33(8): 2358-2361.
7. Koay H V, Chuah J H, Chow C O, et al. Detecting and recognizing driver distraction through various data modality using machine learning: a review, recent advances, simplified framework and open challenges (2014–2021). Eng Appl Artif Intell, 2022, 115(6): 105309.
8. Liu Y, Lan Z, Cui J, et al. Inter-subject transfer learning for EEG-based mental fatigue recognition. Adv Eng Inform, 2020, 46(4): 101157.
9. Zhong L, Xu M, Li J, et al. From micro to meso: a data-driven mesoscopic region division method based on functional connectivity for EEG-based driver fatigue detection. IEEE J Biomed Health Inform, 2025, 29(4): 2603-2616.
10. Zhao P, Lian C, Xu B, et al. Multiscale global prompt transformer for EEG-based driver fatigue recognition. IEEE Trans Autom Sci Eng, 2025, 22: 2700-2711.
11. 楊慧舟, 劉云飛, 夏麗娟. 前額單通道腦電信號的疲勞特征提取及分類算法. 生物醫學工程學雜志, 2024, 41(4): 732-741.
12. Alghanim M, Attar H, Rezaee K, et al. A hybrid deep neural network approach to recognize driving fatigue based on EEG signals. Int J Intell Syst, 2024, 2024: 9898333.
13. 馮笑, 代少升, 黃煉. 基于可解釋深度學習的單通道腦電跨被試疲勞駕駛檢測. 儀器儀表學報, 2023, 44(5): 140-149.
14. Chen X, Hsu C F, Xu D, et al. Loss of frontal regulator of vigilance during sleep inertia: a simultaneous EEG-fMRI study. Hum Brain Mapp, 2020, 41(15): 4288-4298.
15. Li G, Huang S, Xu W, et al. The impact of mental fatigue on brain activity: a comparative study both in resting state and task state using EEG. BMC Neurosci, 2020, 21(1): 20.
16. DaneshmanD H, Kohler J, Bach F, et al. Batch normalization provably avoids ranks collapse for randomly initialised deep networks//Proceedings of the 34th International Conference on Neural Information Processing Systems, Red Hook: Curran Associates Inc, 2020: 18387-18398.
17. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach: NIPS, 2017: 6000-6010.
18. Li J, Liang X, Wu L, et al. Randomness regularization with simple consistency training for neural networks. IEEE Trans Pattern Anal Mach Intell, 2024, 46(8): 5763-5778.
19. Zheng W L, Lu B L. A multimodal approach to estimating vigilance using EEG and forehead EOG. J Neural Eng, 2017, 14(2): 026017.
20. Cao Z, Chuang C H, King J K, et al. Multi-channel EEG recordings during a sustained-attention driving task. Sci Data, 2019, 6(1): 19.
21. Lu Y, Zheng W L, Li B, et al. Combining eye movements and EEG to enhance emotion recognition//Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires: The International Joint Conferences on Artificial Intelligence, 2015: 1170-1176.
22. Gao X Y, Zhang Y F, Zheng W L, et al. Evaluating driving fatigue detection algorithms using eye tracking glasses//2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), Montpellier: IEEE, 2015: 767-770.
23. Cui J, Lan Z, Liu Y, et al. A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG. Methods, 2022, 202: 173-184.
24. Kramer O. K-nearest neighbors//Kramer O. Dimensionality reduction with unsupervised nearest neighbors. Berlin: Springer Berlin Heidelberg, 2013: 13-23.
25. Cutler A, Cutler D R, Stevens J R. Random forests//Zhang C, Ma Y. Ensemble machine learning: methods and applications. New York: Springer, 2012: 157-175.
26. Chen S, Webb G I, Liu L, et al. A novel selective na?ve bayes algorithm. Knowl Based Syst, 2020, 192: 105361.
27. Lederer J. Support-vector machines//Lederer J. A first course in statistical learning: with data examples and python code. Cham: Springer Nature Switzerland, 2025: 187-243.
28. Alhagry S, Aly A, El-khoribi R A. Emotion recognition based on EEG using LSTM recurrent neural network. Int J Adv Comput Sci Appl, 2017, 8(10): 355-358.
29. Eldele E, Chen Z, Liu C, et al. An attention-based deep learning approach for sleep stage classification with single-channel EEG. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 809-818.
30. Song Y, Zheng Q, Liu B, et al. EEG Conformer: convolutional Transformer for EEG decoding and visualization. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 710-719.
31. Gong P, Wang P, Zhou Y, et al. TFAC-net: a temporal-frequential attentional convolutional network for driver drowsiness recognition with single-channel EEG. IEEE Trans Intell Transp Syst, 2024, 25(7): 7004-7016.
32. Ma L, Marshall P J, Wright W G. The impact of external and internal focus of attention on visual dependence and EEG alpha oscillations during postural control. J Neuroeng Rehabil, 2022, 19: 81.

Journal of Biomedical Engineering

A novel mental fatigue detecting method based on single-channel electroencephalogram using hybrid convolutional neural network combined with Transformer

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