EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation

Driver fatigue evaluation is of great importance for traffic safety and many intricate factors would exacerbate the difficulty. In this paper, based on the spatial-temporal structure of multichannel electroencephalogram (EEG) signals, we develop a novel EEG-based spatial-temporal convolutional neural network (ESTCNN) to detect driver fatigue. First, we introduce the core block to extract temporal dependencies from EEG signals. Then, we employ dense layers to fuse spatial features and realize classification. The developed network could automatically learn valid features from EEG signals, which outperforms the classical two-step machine learning algorithms. Importantly, we carry out fatigue driving experiments…

• NN
  National Natural Science Foundation of China

  Awards: 61473203, 61773284, 61873181
• NS
  Natural Science Foundation of Tianjin City

  Award: 16JCYBJC18200