Increase in Regularity and Decrease in Variability seen in Electroencephalogram (EEG) Signals from Alert to Fatigue suring a Driving Simulated Task

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 1096 - 1099
Issue Date:
2008-01
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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.
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