Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy

Ning Ning Zhang; Qiang Zhang

doi:10.4028/www.scientific.net/AMR.779-780.1019

Paper Titles

The Major Problems Needed to be Resolved in the Development of Electric Vehicles at the Present Stage in China
p.991

Diesel Engine Spray Modeling with Lattice Boltzmann Method
p.996

Research on Nozzle Geometry Measurement with Synchrotron Radiation X-Ray Phase Contrast Imaging Technique
p.1007

Analysis of Operation Load Parameters in the Process of Container Transportation
p.1015

Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy
p.1019

Simulation Research on Plain 2-D Numerical Value of Submerged Spur Dike
p.1023

Research on the Model of Road Traffic Safety Evaluation Based on Synergetics Theory
p.1028

Green Transportation and Land Use Planning
p.1036

Inefficiency of Stackelberg Stochastic Traffic Network with Tax Scheme
p.1044

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 779-780Characteristic Extraction of Fatigue Driver's EEG...

Characteristic Extraction of Fatigue Driver's EEG Signals Based on Wavelet Entropy

Abstract:

This study aims to develop a method to detect drivers fatigue using the EEG signals. Experiments have been designed to test the subjects under simulated driving and actual driving, and the fatigue drivers Electroencephalogram (EEG) signals were collected. Wavelet transform method was applied to de-noise the raw EEG data. The H, R (H=α/β; R= (α+θ)/β) wavelet entropy were calculated. The results show that the fatigue drivers H, R wavelet entropy decreased after rest (P<0.05). It is concluded that there are significant difference in brain function between fatigue states and recovered after rest. It is shown that H, R wavelet entropy is an effective eigenvalue to measure drivers fatigue.

You might also be interested in these eBooks

Materials, Transportation and Environmental Engineering

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 779-780)

Pages:

1019-1022

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.779-780.1019

Citation:

Cite this paper

Online since:

September 2013

Authors:

Ning Ning Zhang, Qiang Zhang

Keywords:

Driving Fatigue, EEG, EEG Rhythm, Wavelet Entropy

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] F. Barwick, P. Arnett, S. Slobounov. EEG correlates of fatigue during administration of a neuropsychological test battery. Clinical Neurophysiology 123 (2012), 278-284.

DOI: 10.1016/j.clinph.2011.06.027

Google Scholar

[2] S. Kar, M. Bhagat, A. Routray. EEG signal analysis for the assessment and quantification of driver's fatigue. Transportation Research Part F 13(2010) 297-306.

DOI: 10.1016/j.trf.2010.06.006

Google Scholar

[3] J.P. Liu, C. Zhang, C.X. Zheng. EEG-based estimation of mental fatigue by using KPCA-HMM and complexity parameters. Biomedical Signal Processing and Control 5(2010) 124-130.

DOI: 10.1016/j.bspc.2010.01.001

Google Scholar

[4] M. Simon, E.A. Schmidt, W.E. Kincses. EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions. Clinical Neurophysiology 122(2011) 1168-1178. Transportation Research Part F 13(2010) 297-306.

DOI: 10.1016/j.clinph.2010.10.044

Google Scholar

[5] L. S.K.L., Craig. A., Driver Fatigue: psychophysiological effects. The Fourth International Conference on Fatigue and Transportation, (2000), Australia.

Google Scholar

[6] M.M. Lorist, E. Bezdan, M. Caat, The influence of mental fatigue and motivation on neural network dynamics: an EEG coherence study. Brain Research 1270 (2009) 95-106.

DOI: 10.1016/j.brainres.2009.03.015

Google Scholar

[7] S.B. Wu, L. Gao, L.A. Wang, Detecting Driving Fatigue Based on Electroencephalogram. Transactions of Beijing Institute of Technology 29 (2009) 1072-1075.

Google Scholar

[8] S.K.L. Lal, A. Craig, A critical review of the psychophysiology of driver fatigue. Biological Psychology 55(2001) 173-194.

DOI: 10.1016/s0301-0511(00)00085-5

Google Scholar