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Published on: December 18, 2020
Research on driving fatigue detection based on multi-scale wavelet packet-inspired convolutional network under small
1School of Health and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Abstract:
This study proposes a Multi-scale Wavelet Packet Inspired Convolutional Network (MWPICNet) based on electroencephalogram (EEG) signals for driving fatigue detection. MWPICNet alternates between multi-scale wavelet packet convolution layers and soft threshold activation layers to extract time-frequency fatigue features. It incorporates a frequency band weighting layer and a global power pooling layer to enhance feature recognition. Tested on the SEED-VIG and SADT datasets, MWPICNet achieves accuracies of 98.67% and 99.39%, respectively, maintaining over 95% accuracy under noise interference. Compared to CNN and GCN, MWPICNet improves accuracy by 5.99% and 4.79%.
