The developed EEG-based spatial-temporal convolutional neural network (ESTCNN) achieved a classification accuracy of 97.37% for detecting driver fatigue, outperforming eight competitive methods.
The developed ESTCNN model achieves high accuracy in detecting driver fatigue from EEG signals, offering potential for online brain-computer interface systems.
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 to collect EEG signals from eight subjects being alert and fatigue states. Using 2800 samples under within-subject splitting, we compare the effectiveness of ESTCNN with eight competitive methods. The results indicate that ESTCNN fulfills a better classification accuracy of 97.37% than these compared methods. Furthermore, the spatial-temporal structure of this framework advantages in computational efficiency and reference time, which allows further implementations in the brain-computer interface online systems.
Gao et al. (Thu,) conducted a other in Driver fatigue (n=8). EEG-based spatial-temporal convolutional neural network (ESTCNN) vs. Eight competitive methods was evaluated on Classification accuracy. The developed EEG-based spatial-temporal convolutional neural network (ESTCNN) achieved a classification accuracy of 97.37% for detecting driver fatigue, outperforming eight competitive methods.