Statistics analysis and eigenvalue construction of EEG for driving fatigue by fuzzy entropy arithmetic

In this paper, electroencephalograms (EEG) of drivers driving under normal and fatigue state are collected and decomposed into 4 components, d1, d2, d3 and d4, by way of empirical mode decomposition (EMD). And Fuzzy Entropy arithmetic was used to analyze the characteristic of the components. Results show that fuzzy entropy ratio of d2 and d4 was able to distinguish the two types of EEG with a favorably agreed difference, which could decide whether or not the driver is in driving fatigue (DF).