TY - GEN
T1 - Driving fatigue detection based on EEG signal
AU - Wang, Yuan
AU - Liu, Xin
AU - Zhang, Yan
AU - Zhu, Zheng
AU - Liu, Dan
AU - Sun, Jinwei
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/11
Y1 - 2016/2/11
N2 - 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 eigenvalues 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%.
AB - 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 eigenvalues 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%.
KW - BP neural network
KW - Driving fatigue
KW - EEG acquisition
KW - Interference process
UR - https://www.scopus.com/pages/publications/84963974369
U2 - 10.1109/IMCCC.2015.156
DO - 10.1109/IMCCC.2015.156
M3 - 会议稿件
AN - SCOPUS:84963974369
T3 - Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
SP - 715
EP - 718
BT - Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
A2 - Li, Jun-Bao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
Y2 - 18 September 2015 through 20 September 2015
ER -