Mohaymany, Afshin Shariat Kashani, Ali Tavakoli Ranjbari, Andishe Identifying driver characteristics influencing overtaking crashes. 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). This technique reduces the time required for training by modifying the standard back propagation (SBP) algorithm. The neural network uses a training optimization technique called the magnified gradient function (MGF). 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. This paper describes a driver fatigue detection system using an artificial neural network (ANN). Early driver fatigue detection from electroencephalography signals using artificial neural networks.
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