A regression method for EEG-based cross-dataset fatigue detection - Yuan, Duanyang; Yue, Jingwei; Xiong, Xuefeng; Jiang, Yibi; Zan, Peng; Li, Chunyong (Frontiers in Physiology, 2023)
Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of 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 before. Therefore, this study will focus on the design of the cross-dataset fatigue detection model. 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 domain-specific adaptive. To extract specific features for different datasets, a pretext task is proposed to distinguish data of different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. And the MMD is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. Besides, the attention mechanism is introduced to extract continuous information on spatial features, and GRU is used to capture time series information. The accuracy and RMSE achieved by the proposed method are 59.10% and 0.27, which significantly outperforms state-of-the-art domain adaptation methods. 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. And the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research.