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EEG classification of driver mental states by deep learning

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Abstract

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 .

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Acknowledgements

The authors would also like to thank the anonymous referees for their valuable comments and helpful suggestions. The work is supported by the National Natural Science Foundation of China under Grant Nos. {61671193, 61633010, 61473110, 61502129}, Key Research and Development Plan of Zhejiang Province under Grant No. 2018C04012, Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ16F020004. Science and technology platform construction project of Fujian science and Technology Department No. 2015Y2001.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Hong Zeng, Chen Yang, Guojun Dai, Feiwei Qin, Jianhai Zhang & Wanzeng Kong

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  1. Hong Zeng
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  2. Chen Yang
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  3. Guojun Dai
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  4. Feiwei Qin
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  5. Jianhai Zhang
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  6. Wanzeng Kong
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Correspondence to Wanzeng Kong.

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Zeng, H., Yang, C., Dai, G. et al. EEG classification of driver mental states by deep learning. Cogn Neurodyn 12, 597–606 (2018). https://doi.org/10.1007/s11571-018-9496-y

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