Recent Advances in Fatigue Detection Algorithm Based on EEG

被引:10
作者
Wang, Fei [1 ,2 ]
Wan, Yinxing [1 ]
Li, Man [1 ,2 ]
Huang, Haiyun [1 ,2 ]
Li, Li [1 ]
Hou, Xueying [1 ]
Pan, Jiahui [1 ,2 ]
Wen, Zhenfu [3 ]
Li, Jingcong [1 ,2 ]
机构
[1] South China Normal Univ, Sch Software, Foshan 528225, Peoples R China
[2] Pazhou Lab, Guangzhou 510330, Peoples R China
[3] NYU, Sch Med, Dept Psychiat, New York, NY 10016 USA
基金
中国国家自然科学基金;
关键词
EEG; fatigue detection; deep learning; machine learning; transfer learning; DRIVER FATIGUE; AUTOMATED DETECTION; SYSTEM; SIGNALS; REGULARIZATION; REGRESSION; DESIGN; MODEL;
D O I
10.32604/iasc.2023.029698
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fatigue is a state commonly caused by overworked, which seriously affects daily work and life. How to detect mental fatigue has always been a hot spot for researchers to explore. Electroencephalogram (EEG) is considered one of the most accurate and objective indicators. This article investigated the devel-opment of classification algorithms applied in EEG-based fatigue detection in recent years. According to the different source of the data, we can divide these classification algorithms into two categories, intra-subject (within the same sub-ject) and cross-subject (across different subjects). In most studies, traditional machine learning algorithms with artificial feature extraction methods were com-monly used for fatigue detection as intra-subject algorithms. Besides, deep learn-ing algorithms have been applied to fatigue detection and could achieve effective result based on large-scale dataset. However, it is difficult to perform long-term calibration training on the subjects in practical applications. With the lack of large samples, transfer learning algorithms as a cross-subject algorithm could promote the practical application of fatigue detection methods. We found that the research based on deep learning and transfer learning has gradually increased in recent years. But as a field with increasing requirements, researchers still need to con-tinue to explore efficient decoding algorithms, design effective experimental para-digms, and collect and accumulate valid standard data, to achieve fast and accurate fatigue detection methods or systems to further widely apply.
引用
收藏
页码:3573 / 3586
页数:14
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