Early driver fatigue detection from electroencephalography signals using artificial neural networks.
This paper describes a driver fatigue detection system using an artificial neural network (ANN). Using electroencephalogram (EEG) data sampled from 20 professional truck drivers and 35 non professional drivers, the time domain data are processed into alpha, beta, delta and theta bands and then presented to the neural network to detect the onset of driver fatigue. The neural network uses a training optimization technique called the magnified gradient function (MGF). This technique reduces the time required for training by modifying the standard back propagation (SBP) algorithm. The MGF is shown to classify professional driver fatigue with 81.49% accuracy (80.53% sensitivity, 82.44% specificity) and non-professional driver fatigue with 83.06% accuracy (84.04% sensitivity and 82.08% specificity).
King
L M
LM
Key Univ. Res. Centre for Health Technol., Univ. of Technol., Sydney, NSW, Australia.
Nguyen
H T
HT
Lal
S K L
SK
D000328:Adult; D000465:Algorithms; D001334:Automobile Driving; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D005222:Mental Fatigue / Q000175:diagnosis* / Q000503:physiopathology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D013647:Task Performance and Analysis