A regression method for EEG-based cross-dataset fatigue detection

Frontiers in Physiology

Physiology

Chunyong Li¹  Jingwei Yue¹  Peng Zan²  Xuefeng Xiong²  Yibi Jiang²  Duanyang Yuan² 

[1] Beijing Institute of Radiation Medicine, Academy of Military Medical Sciences (AMMS), Beijing, China;Shanghai Key Laboratory of Power Station Automation, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;

关键词: fatigue detection;    cross-dataset;    EEG;    regression method;    self-supervised learning;   

DOI  :  10.3389/fphys.2023.1196919

 received in 2023-03-30, accepted in 2023-05-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model.Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information.Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods.Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices.

【 授权许可】

Unknown   
Copyright © 2023 Yuan, Yue, Xiong, Jiang, Zan and Li.