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Cognitive Neurodynamics

, Volume 12, Issue 4, pp 365–376 | Cite as

A novel real-time driving fatigue detection system based on wireless dry EEG

  • Hongtao Wang
  • Andrei Dragomir
  • Nida Itrat Abbasi
  • Junhua LiEmail author
  • Nitish V. Thakor
  • Anastasios BezerianosEmail author
Research Article

Abstract

Development of techniques for detection of mental fatigue has varied applications in areas where sustaining attention is of critical importance like security and transportation. The objective of this study is to develop a novel real-time driving fatigue detection methodology based on dry Electroencephalographic (EEG) signals. The study has employed two methods in the online detection of mental fatigue: power spectrum density (PSD) and sample entropy (SE). The wavelet packets transform (WPT) method was utilized to obtain the \(\theta \) (4–7 Hz), \(\alpha \) (8–12 Hz) and \(\beta \) (13–30 Hz) bands frequency components for calculating corresponding PSD of the selected channels. In order to improve the fatigue detection performance, the system was individually calibrated for each subject in terms of fatigue-sensitive channels selection. Two fatigue-related indexes: (\(\theta +\alpha \))/\(\beta \) and \(\theta \)/\(\beta \) were computed and then fused into an integrated metric to predict the degree of driving fatigue. In the case of SE extraction, the mean of SE averaged across two EEG channels (‘O1h’ and ‘O2h’) was used for fatigue detection. Ten healthy subjects participated in our study and each of them performed two sessions of simulated driving. In each session, subjects were required to drive simulated car for 90 min without any break. The results demonstrate that our proposed methods are effective for fatigue detection. The prediction of fatigue is consistent with the observation of reaction time that was recorded during simulated driving, which is considered as an objective behavioral measure.

Keywords

Driving fatigue Electroencephalogram Dry electrodes PSD and entropy Channel selection 

Notes

Acknowledgements

This study was supported by the Defence Science Organisation (DSO) of Singapore under Grant Number R-719-000-027-592, Technology Development Project of Guangdong Province (No. 2017A010101034), Innovation Projects for Science supported by Department of Education of Guangdong Province (No. 2016KTSCX141), Science Foundation for Young Teachers of Wuyi University (No. 2018td02), Jiangmen Research and Development Program ([2017]268) and the China Scholarship Council ([2016]5113).

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Copyright information

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

Authors and Affiliations

  • Hongtao Wang
    • 1
    • 2
  • Andrei Dragomir
    • 1
  • Nida Itrat Abbasi
    • 1
    • 3
  • Junhua Li
    • 1
    Email author
  • Nitish V. Thakor
    • 1
  • Anastasios Bezerianos
    • 1
    Email author
  1. 1.Singapore Institute for Neurotechnology(SINAPSE), Centre for Life SciencesNational University of SingaporeSingaporeSingapore
  2. 2.School of Information EngineeringWuyi UniversityJiangmenChina
  3. 3.Department of Biomedical EngineeringNational University of SingaporeSingaporeSingapore

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