A New Strategy for Mental Fatigue Detection Based on Deep Learning and Respiratory Signal

Wang, Jie; Shi, Ji‐Long; Xu, Yanting; Zhong, Hongyang; Li, Gang; Tian, Jinghong; Xu, Wanxiu; Gao, Zhao; Jiang, Yonghua; Jiao, Weidong; Tang, Chao

doi:10.1007/978-981-16-6554-7_60

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Some typical deep learning algorithms are used in comparison with our model for one-dimensional feature vector classification, including one-dimensional CNN (1D CNN) [ 55 ], LSTM [ 56 ], Bidirectional LSTM(BiLSTM) [ 57 ], Gate Recurrent Unit (GRU) [ 58 ], Bidirectional GRU (BiGRU) [ 59 ]. Brief descriptions for each baseline are as follows.…”

Section: Methodsmentioning

confidence: 99%

Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model

Wang

Tian

et al. 2022

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Driving fatigue is the main cause of traffic accidents, which seriously affects people’s life and property safety. Many researchers have applied electroencephalogram (EEG) signals for driving fatigue detection to reduce negative effects. The main challenges are the practicality and accuracy of the EEG-based driving fatigue detection method when it is applied on the real road. In our previous study, we attempted to improve the practicality of fatigue detection based on the proposed non-hair-bearing (NHB) montage with fewer EEG channels, but the recognition accuracy was only 76.47% with the random forest (RF) model. In order to improve the accuracy with NHB montage, this study proposed an improved transformer architecture for one-dimensional feature vector classification based on introducing the Gated Linear Unit (GLU) in the Attention sub-block and Feed-Forward Networks (FFN) sub-block of a transformer, called GLU-Oneformer. Moreover, we constructed an NHB-EEG-based feature set, including the same EEG features (power ratio, approximate entropy, and mutual information (MI)) in our previous study, and the lateralization features of the power ratio and approximate entropy based on the strategy of brain lateralization. The results indicated that our GLU-Oneformer method significantly improved the recognition performance and achieved an accuracy of 86.97%. Our framework demonstrated that the combination of the NHB montage and the proposed GLU-Oneformer model could well support driving fatigue detection.

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