A TSK-type Convolutional Recurrent Fuzzy Network for Predicting Driving Fatigue
Driver fatigue monitoring is very important for driving safety, and many intricate factors in driving make fatigue monitoring harder. To effectively predict driving fatigue, this paper proposes a new deep learning framework called TSK-type Convolution Recurrent Fuzzy Network (TCRFN) based on the spatial and temporal characteristics of 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 (CNN) is used to reduce the impact of noise. Additionally, a new local feedback method in Fuzzy Neural Network (FNN) 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 anti-noise performance and prediction accuracy compared with other widely-used and state-of-the-art models.
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