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 neura...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 30; číslo 9; s. 2755 - 2763
Hlavní autori: Gao, Zhongke, Wang, Xinmin, Yang, Yuxuan, Mu, Chaoxu, Cai, Qing, Dang, Weidong, Zuo, Siyang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Artificial neural networks
Brain
Brain modeling
Brain–computer interface (BCI)
Classification
Computational neuroscience
Convolution
convolutional neural network (CNN)
deep learning (DL)
Driver fatigue
EEG
electroencephalogram (EEG)
Electroencephalography
Evaluation
Fatigue
fatigue driving
Feature extraction
Learning algorithms
Machine learning
Multichannel communication
Neural networks
On-line systems
Physiology
spatio–temporal data
Task analysis
Traffic accidents & safety
Traffic safety
ISSN:2162-237X, 2162-2388, 2162-2388
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Shrnutí: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.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2018.2886414