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Cognitive Neurodynamics
Erschienen in: Cognitive Neurodynamics 6/2018

18.07.2018 | Research Article

EEG classification of driver mental states by deep learning

verfasst von: Hong Zeng, Chen Yang, Guojun Dai, Feiwei Qin, Jianhai Zhang, Wanzeng Kong

Erschienen in: Cognitive Neurodynamics | Ausgabe 6/2018

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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 .
Vorheriger Artikel Higher-order spectral analysis of spontaneous speech signals in Alzheimer’s disease
Nächster Artikel Complex dynamics of a neuron model with discontinuous magnetic induction and exposed to external radiation

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Metadaten
Titel
EEG classification of driver mental states by deep learning
verfasst von
Hong Zeng
Chen Yang
Guojun Dai
Feiwei Qin
Jianhai Zhang
Wanzeng Kong
Publikationsdatum
18.07.2018
Verlag
Springer Netherlands
Erschienen in
Cognitive Neurodynamics / Ausgabe 6/2018
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-018-9496-y

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