Journal of Northeastern University Natural Science ›› 2014, Vol. 35 ›› Issue (2): 175-178.DOI: 10.12068/j.issn.1005-3026.2014.02.006

• Information & Control • Previous Articles     Next Articles

Comprehensive Analysis of Fatigue Driving Based on EEG and EOG

WANG Fuwang, WANG Hong, LUO Xu   

  1. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China.
  • Received:2013-05-02 Revised:2013-05-02 Online:2014-02-15 Published:2013-11-22
  • Contact: WANG Hong
  • About author:-
  • Supported by:
    -

Abstract: The EEG and EOG have significant changes when drivers are fatigued. So these two types of signals can be used to analyze fatigue driving. Firstly, the α rhythm was extracted from drivers’ EEG signals using wavelet packet decomposition and its relative power spectrum P was calculated. Then the blinking characteristics of the EOG signals contained in F7 and F8 channels were analyzed and the interference signals were removed using the Pearson correlation coefficient. Finally, the blinking signals were identified using BP neural network and the blinking rate was calculated. The results show that using the comprehensive analysis of EOG and EEG can accurately identify the blinking signals and correctly detect the changes of driver fatigue state.

Key words: fatigue driving, EEG(electroencephalogram), EOG(electroencephalogram), wavelet packet decomposition, relative power spectrum, blinking rate

CLC Number: