Research on Fatigue Driving Recognition Based on Brain Function Network
FU Rong-rong1,MI Rui-fu1,WANG Han1,YU Bao1,WANG Lin2
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. School of Mechanical Engineering, Shenyang Institute of Engineering, Shenyang, Liaoning 110136, China
Abstract：To effectively identify the drivers fatigue status, a real highway driving experiment is conducted. Collecting multi-channel electroencephalograph (EEG) of drivers at different times through wireless EEG acquisition equipment. Based on the phase lag index, an adjacency matrix and a binary matrix are established. A brain function network and its corresponding topographic map are obtained. The complex network theory is used to calculate and analyze the characteristic parameters of the brain function network node-degree and compare the node degree at different moments. According to the variation trend of each node degree and the driver's subjective judgment, it is found that as the driving experiment continues, the degree of driving fatigue increases, the information processing ability of the brain decreases, and the decrease trend of phase lag index value and the node degree is obvious. It shows that the degree of brain function network can be used as an objective index of brain fatigue. Through comparison with other detection methods, it is obtained that using the node degree as the evaluation index of brain fatigue is more reliable.
付荣荣,米瑞甫,王涵,于宝,王琳. 基于脑功能网络的脑疲劳状态检测研究[J]. 计量学报, 2021, 42(11): 1528-1533.
FU Rong-rong,MI Rui-fu,WANG Han,YU Bao,WANG Lin. Research on Fatigue Driving Recognition Based on Brain Function Network. Acta Metrologica Sinica, 2021, 42(11): 1528-1533.
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2021, Vol. 42 
