On-line automatic detection of driver drowsiness using a single electroencephalographic channel.
In this paper, an on-line drowsiness detection algorithm using a single electroencephalographic (EEG) channel is presented. This algorithm is based on a means comparison test to detect changes of the alpha relative power ([8-12]Hz band). The main advantage of the method proposed is that the detection threshold is completely independent of drivers and does not need to be tuned for each person. This algorithm, which works on-line, has been tested on a huge dataset representing 60 hours of driving and give good results with nearly 85% of good detections and 20% of false alarms.
Picot
Antoine
A
Gipsa-lab, 961 rue de la Houille Blanche, Domaine Universitaire, Saint Martin de Heres Cedex, France. antoine.picot@gipsa-lab.inpg.fr
Charbonnier
Sylvie
S
Caplier
Alice
A
D000063:Accidents, Traffic / Q000517:prevention & control*; D000465:Algorithms; D001331:Automation; D001334:Automobile Driving; D016208:Databases, Factual; D004569:Electroencephalography / Q000379:methods*; D001330:Electronic Data Processing; D005189:False Positive Reactions; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D012449:Safety; D012894:Sleep Stages