Development of an algorithm for an EEG-based driver fatigue countermeasure.
OBJECTIVE
Fatigue affects a driver's ability to proceed safely. Driver-related fatigue and/or sleepiness are a significant cause of traffic accidents, which makes this an area of great socioeconomic concern. Monitoring physiological signals while driving provides the possibility of detecting and warning of fatigue. The aim of this paper is to describe an EEG-based fatigue countermeasure algorithm and to report its reliability.


METHODS
Changes in all major EEG bands during fatigue were used to develop the algorithm for detecting different levels of fatigue.


RESULTS
The software was shown to be capable of detecting fatigue accurately in 10 subjects tested. The percentage of time the subjects were detected to be in a fatigue state was significantly different than the alert phase (P<.01).


CONCLUSIONS
This is the first countermeasure software described that has shown to detect fatigue based on EEG changes in all frequency bands. Field research is required to evaluate the fatigue software in order to produce a robust and reliable fatigue countermeasure system.


CONCLUSIONS
The development of the fatigue countermeasure algorithm forms the basis of a future fatigue countermeasure device. Implementation of electronic devices for fatigue detection is crucial for reducing fatigue-related road accidents and their associated costs.
Lal
Saroj K L
SK
Department of Health Sciences, University of Technology, Sydney, Floor 14, Broadway, 2007, NSW, Sydney, Australia. sara.lal@utu.edu.au
Craig
Ashley
A
Boord
Peter
P
Kirkup
Les
L
Nguyen
Hung
H
D000063:Accidents, Traffic / Q000517:prevention & control; D000465:Algorithms; D001315:Australia; D001334:Automobile Driving / Q000523:psychology*; D004569:Electroencephalography; D005221:Fatigue / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D015203:Reproducibility of Results; D012449:Safety; D012984:Software