Nonlinear Dynamics Of Driving Fatigue Eeg Analysis

Currently, driving fatigue has been one of the main reasons of traffic accidents, bringing terrible economic lost and large scale of damages. In this case, detecting driving fatigue efficiently is of great importance to prevent and control driving fatigue.Researches show that electroencephalogram (EEG) could correctly reflect physical conditions and regarded as a gold standard for evaluating fatigue. A large number of scholars study the driving fatigue by EEG and made some improvements. However, these studies mainly focus on time domain, or frequency domain, which is not quite effective to non-linear signals like EEG. Therefore, this article take the angle of non-linear dynamic to analyze the EEG that colleted from 12 subjects in the process of a driving simulation, trying to study the characters of EEG during driving. The main work of this paper is:First, threshold of wavelet transform is used to denoise the EEG in order to raise the accuracy of the analysis.Second, phase space reconstruct theory is used to reconstruct the statement of fatigue and non-fatigue. Then calculate the different correlation dimension and Lyapunov index values of 12 subjects in vary driving statements. The analysis shows that correlation dimension and Lyapunov index value are both on different in different statements. They are available to distinguish different driving statements.At last, calculate the corresponding values of the subjects in different driving statements with c0 complexity, approximate entropy, sample entropy and multi-scale entropy. The calculations show that the results of all the methods used are different in different statements. Approximate entropy is more obvious than c0 complexity and the multi-scale entropy is more obvious than the approximate entropy and the sample entropy.Therefore, the correlation dimension, Lyapunov index, c0 complexity, approximate entropy, sample entropy and multi-scale entropy are available to be an indication of detecting driving fatigue, only different in the detecting results.