Paper Info
Title
EEG-Based Spatio-Temporal Convolutional Neural Network for Driver Fatigue Evaluation.
Abstract
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.
Year
DOI
Venue
2019
10.1109/TNNLS.2018.2886414
IEEE transactions on neural networks and learning systems
Keywords
Field
DocType
Electroencephalography,Fatigue,Convolution,Feature extraction,Brain modeling,Task analysis,Physiology
Pattern recognition,Task analysis,Convolutional neural network,Computer science,Convolution,Feature extraction,Implementation,Artificial intelligence,Fuse (electrical),Electroencephalography
Journal
Volume
Issue
ISSN
30
9
2162-2388
Citations 
PageRank 
References 
15
0.68
0
Authors
7
Name
Order
Citations
PageRank
Zhongke Gao1598.79
Xinmin Wang2211.94
Yuxuan Yang3635.78
C. Mu413110.88
qing cai5608.64
Wei-Dong Dang6253.60
Siyang Zuo7191.86