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

By Zhongke Gao, Xinmin Wang, Yuxuan Yang, Chaoxu Mu, Qing Cai, Weidong Dang, Siyang Zuo. IEEE Transactions on Neural Networks and Learning Systems, 2019

412 citations recorded by the source corpus.

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.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/98a837351ead702482487c6c74f85d79f1ed5932

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Mathias Baumert
2. Cuntai Guan

In Cognitive Neuroscience

1. Yi Ding
2. Qiuhao Zeng
3. Su Zhang
4. Neethu Robinson
5. Ganesh R. Naik
6. Cuntai Guan
7. Fabien Lotte
8. Dongrui Wu

Citations in the corpus

• TSception: Capturing Temporal Dynamics and Spatial Asymmetry From EEG for Emotion Recognition (2021, methodology): “…fusion to detect emotions from EEG. Recently, deep learning-based methods have shown promising results in the BCI domain, such as motor imagery clas-sification [15], [16], [17], [18], [19], emotion recognition [20], [21], [22], [23], [24], [25], and mental-task classification [26], [27], [28].”
• Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “Detecting driver’s drowsiness to prevent road accidents is an example of passive BCI (Lin et al., 2008; Gao et al., 2019).”
• EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, neutral): “[98] for driver fatigue detection.”