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Paper Title:
Towards the development
of a countermeasure device to detect fatigue in drivers from electroencephalography
signals
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full paper
Authors:
Saroj Lal, Ashley Craig
Abstract:
Fatigue affects the drivers'
ability to continue driving safely. Therefore, on-line monitoring
of physiological signals while driving provides the possibility
of detecting fatigue in real time. The EEG signal has been found
to be the most predictive and reliable indicator. However, little
evidence exists on implementing EEG into a fatigue countermeasure
device.
The aims were to utilise
EEG changes during fatigue for development of fatigue countermeasure
software and to test the ability of such software in detecting fatigue.
EEG was obtained in twenty truck drivers during a driver simulator
task till subjects fatigued. Changes found in delta, theta, alpha
and beta activity were used to develop algorithms for the software.
The software was designed to detect an alert state and early, medium
and extreme levels of fatigue. The software was tested in off-line
mode in a group of ten truck drivers.
The software was capable
of detecting fatigue accurately in all ten subjects. The percentage
of time the subjects were detected to be in a fatigue state was
significantly different to the alert phase (p<0.01). For 40%
of the total driving time subjects were alert and for 60% of the
time, the software detected one of the three fatigue states. In
on-line analysis the software could alert the three stages of fatigue.
The software could detect fatigue accurately. This is the first
countermeasure software that can detect fatigue based on EEG changes
in all bands. Future field research is required with the fatigue
software to produce a robust and reliable fatigue countermeasure
system.
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full paper
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