Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model

Brief Communication

Abstract

Driver fatigue is increasingly a contributing factor for traffic accidents, so an effective method to automatically detect driver fatigue is urgently needed. In this study, in order to catch the main characteristics of the EEG signals, four types of entropies (based on the EEG signal of a single channel) were calculated as the feature sets, including sample entropy, fuzzy entropy, approximate entropy and spectral entropy. All feature sets were used as the input of a gradient boosting decision tree (GBDT), a fast and highly accurate boosting ensemble method. The output of GBDT determined whether a driver was in a fatigue state or not based on their EEG signals. Three state-of-the-art classifiers, k-nearest neighbor, support vector machine and neural network were also employed. To assess our method, several experiments including parameter setting and classification performance comparison were performed on 22 subjects. The results indicated that it is possible to use only one EEG channel to detect a driver fatigue state. The average highest recognition rate in this work was up to 94.0%, which could meet the needs of daily applications. Our GBDT-based method may assist in the detection of driver fatigue.

Keywords

Driver fatigue Electroencephalogram (EEG) Gradient boosted decision tree (GBDT) Entropy 

Notes

Acknowledgements

This work was supported by Supported by National Natural Science Foundation of China (61762045), Natural Science Foundation of Jiangxi Province, China (Nos. 20151BBE50079, 20171BAB202031), Educational Commission of Jiangxi Province, China (Nos. GJJ151146, GJJ161143) and Postdoctoral Assistance Project of Jiangxi Province, China (2017KY33). Thanks ZD Mu and P Wang for collecting and preprocessing EEG data.

Compliance with ethical standards

Conflict of interest

The author declares no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Center of Collaboration and InnovationJiangxi University of TechnologyNanchangChina

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