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Updated: Sep 23, 2025
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical
Fuwang Wang1, Xiaolei Zhang1, Rongrong Fu2
1School of Mechanic Engineering, Northeast Electric Power University Jilin 132012 China wangfuwangfeixue@163.com 20152622@neepu.edu.cn wangfuwangbaiyang@126.com +86-432-64807382.
Abstract:
This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment. First, the θ, β and 36-44 Hz rhythm from the EEG signals of drivers were extracted using wavelet packet decomposition (WPD). The correlation between EEG channels was calculated using a Pearson correlation coefficient and subsequently, the brain networks were built. Furthermore, the clustering coefficient (C) and global efficiency (G) of the complex brain networks were calculated to analyze the functional differences in the brains of drivers over time. Combined with the relative power spectrum ratio (β/θ) of EEG signals and the mean value from questionnaires, the correlation of data characteristics between brain networks and subjective and objective data was analyzed. The results show that changes in the fatigue state of drivers can be effectively detected by calculating the data characteristics of brain networks in a real driving environment.
