Abstract：With the advancement of electroencephalogram (EEG) technology, EEG-based fatigue monitoring models have become crucial tools for ensuring safety and improving efficiency in high-risk fields such as transportation and medical surgeries. These models provide objective fatigue assessments and early warnings, reducing human error and accident risks. However, their application in real-world scenarios faces challenges, including issues with the comfort and convenience of EEG acquisition, motion artifacts and electromyographic noise interfering with feature extraction, and variability in EEG signals both between and within individuals, which results in the complication of model generalization. Additionally, the lack of standardization in fatigue data labeling and collection leads to inconsistencies and biases. This review summarized these challenges and explored potential solutions, aiming to advance the practical application of EEG-based fatigue monitoring systems. Along with the progress of EEG acquisition technologies, signal processing algorithms and machine learning models, the accuracy, convenience and generalizability would be continuously improved in the future systems, enhancing work safety and efficiency.
王韬, 何峰. 基于脑电信号的任务诱发疲劳监测的关键挑战与展望[J]. 中国生物医学工程学报, 2025, 44(2): 221-231.
Wang Tao, He Feng. Key Challenges in Task-Induced Fatigue Monitoring Based on EEG Signals. Chinese Journal of Biomedical Engineering, 2025, 44(2): 221-231.
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