Why the study?
Driver fatigue monitoring is critical for driving safety, but intricate factors during driving make fatigue monitoring challenging.
A novel deep learning framework (TCRFN) improves the prediction accuracy and antinoise performance of driving fatigue monitoring using EEG signals.
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Advances EEG fatigue monitoring models; leaves open prospective clinical validation before practice change.
Driver fatigue monitoring is very important for driving safety, and many intricate factors while driving make fatigue monitoring harder. To effectively predict driving fatigue, this article proposes a new deep learning framework called TSK-type convolution recurrent fuzzy network (TCRFN) based on the spatial and temporal characteristics of electroencephalogram (EEG) signals. In TCRFN, the convolution block is first introduced to extract spatial dependencies from EEG signals. Furthermore, since EEG noise has a strong spatial dependence, this convolutional neural networks is used to reduce the impact of noise. Additionally, a new local feedback method in fuzzy neural network is proposed to process the EEG signals, which can better capture the temporal dependencies from EEG signals. Finally, a logarithmic spatial firing layer function is used in the proposed TCRFN. The activation performance of this function is smoother, which allows more feature numbers and provides better prediction. The experimental results show that the proposed TCRFN model has better antinoise performance and prediction accuracy compared with other widely used and state-of-the-art models.
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Du et al. (2020) studied this question.
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