Citation

Tran Y, Wijesuryia N, Thuraisingham RA, Craig A, Nguyen HT. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008; 2008: 1096-1099.

Affiliation

Key University Research Centre in Health Technologies, University of Technology, Sydney, Australia.

Copyright

(Copyright © 2008, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/IEMBS.2008.4649351

PMID

19162854

Abstract

Driver fatigue is a prevalent problem and a major risk for road safety accounting for approximately 20-40% of all motor vehicle accidents. One strategy to prevent fatigue related accidents is through the use of countermeasure devices. Research on countermeasure devices has focused on methods that detect physiological changes from fatigue, with the fast temporal resolution from brain signals, using the electroencephalogram (EEG) held as a promising technique. This paper presents the results of nonlinear analysis using sample entropy and second-order difference plots quantified by central tendency measure (CTM) on alert and fatigue EEG signals from a driving simulated task. Results show that both sample entropy and second-order difference plots significantly increases the regularity and decreases the variability of EEG signals from an alert to a fatigue state.

Language: en