EEG classification of driver mental states by deep learning

• Published in: Cognitive neurodynamics Vol. 12; no. 6; pp. 597 - 606
• Main Authors: Zeng, Hong, Yang, Chen, Dai, Guojun, Qin, Feiwei, Zhang, Jianhai, Kong, Wanzeng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2018; Springer Nature B.V
• Subjects: Accuracy; Artificial Intelligence; Artificial neural networks; Biochemistry; Biomedical and Life Sciences; Biomedicine; Classification; Classifiers; Cognitive Psychology; Computer Science; Deep learning; Driver fatigue; EEG; Electrocardiography; Electroencephalography; Entropy; Experiments; Eye movements; Fatigue; Heart rate; Machine learning; Neural networks; Neurosciences; Physiology; Research Article; Simulation; Software; Support vector machines; Traffic accidents; Driver fatigue; EEG-Conv-R; Electroencephalography (EEG); Residual learning; EEG-Conv
• ISSN: 1871-4080; 1871-4099
• DOI: 10.1007/s11571-018-9496-y

Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .