可见性图
聚类系数
计算机科学
脑电图
能见度
图形
聚类分析
人工智能
模式识别(心理学)
任务(项目管理)
GSM演进的增强数据速率
心理学
神经科学
数学
工程类
物理
几何学
系统工程
理论计算机科学
正多边形
光学
作者
Qing Cai,Zhongke Gao,Yuxuan Yang,Weidong Dang,Celso Grebogi
标识
DOI:10.1142/s0129065718500570
摘要
Driver fatigue is an important contributor to road accidents, and driver fatigue detection has attracted a great deal of attention 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 in not only detecting fatigue driving but also probing into the brain fatigue behavior. Importantly, we use the 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.
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