Driving Fatigue Detection Based on EEG Signal

Driving fatigue detection is an important approach to ensure the traffic safety. However, the most existing mature analysis methods are based on driving behavior or driver's body characteristics, which leads to the low accuracy and predictability. The EEG signal analysis is proved to an effective way to reflect the fatigue state in medical science, thus this paper explores the EEG signal to detect the driving fatigue. We design a portable EEG acquisition system, which detects the drivers' EEG signals and handles the interference by the median filter, band stop filter and Hilbert-Huang transform. The eigen values are extracted by percentage power spectral density. Two methods are proposed to determine the fatigue levels. Experiment results show that the method based on eigenvalue ratio in eyes-open state has 79% accuracy, the method based on BP neural network in fatigue classification has 83% accuracy, and the eyes-close state recognition rate is more than 97%.

[1]  Ning-Han Liu,et al.  Improving Driver Alertness through Music Selection Using a Mobile EEG to Detect Brainwaves , 2013, Sensors.

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Ning-Han Liu,et al.  Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors , 2013, Sensors.

[4]  P. de Chazal,et al.  A parametric feature extraction and classification strategy for brain-computer interfacing , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  H.T. Nguyen,et al.  Increase in regularity and decrease in variability seen in electroencephalography (EEG) signals from alert to fatigue during a driving simulated task , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.