Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection

Qing Cai, Zhong-Ke Gao (Corresponding Author), Yu-Xuan Yang, Wei-Dong Dang, Celso Grebogi

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40 Citations (Scopus)
31 Downloads (Pure)


Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attentions on account of its significant importance. Numerous methods have been proposed to fulfill this challenging task, though, the characterization of the fatigue mechanism still, to a large extent, remains to be investigated. To address this problem, we in this work, develop a novel Multiplex Limited Penetrable Horizontal Visibility Graph (Multiplex LPHVG) method, which allows not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use our method to construct brain networks from EEG signals recorded from different subjects performing simulated driving tasks under alert and fatigue driving states. We then employ clustering coefficient, global efficiency and characteristic path length to characterize the topological structure of the networks generated from different brain states. In addition, we combine average edge overlap with the network measures to distinguish alert and mental fatigue states. The high-accurate classification results clearly demonstrate and validate the efficacy of our multiplex LPHVG method for the fatigue detection from EEG signals. Furthermore, our findings show a significant increase of the clustering coefficient as the brain evolves from alert state to mental fatigue state, which yields novel insights into the brain behavior associated with fatigue driving.
Original languageEnglish
Article number1850057
JournalInternational Journal of Neural Systems
Issue number5
Early online date18 Feb 2019
Publication statusPublished - 1 Jun 2019

Bibliographical note

This work was supported by National Natural Science Foundation of China under Grant Nos. 61473203, 61873181 and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200.


  • multiplex limited penetrable horizontal visibility graph
  • EEG
  • brain network
  • driver fatigue detection
  • Multiplex limited penetrable horizontal visibility graph


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