Fatigue Driving Vigilance Detection Using Convolutional Neural Networks and Scalp EEG Signals

Abstract

Fatigue driving is one of the important factors that cause traffic accidents. To solve this problem, this paper proposes a classification model based on the traditional convolutional neural network (CNN) to distinguish the vigilance state. First, the raw electroencephalogram (EEG) signals were converted into two-dimensional spectrograms by the short-time Fourier transform (STFT). Then, the CNN model was used for automatic features extraction and classification from these spectrograms. Finally, the performance of the trained CNN model was evaluated. The average of area under ROC Curve (AUC) was 1, the sensitivity was 91.4%, the average false prediction rate (FPR) was 0.02/h, and the accuracy rate was as high as 97%. The effectiveness of the CNN model was verified by the evaluation results.