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The Analysis of EEG Signals in Driving Behavior Based on Nonlinear Dynamics

When the driver is driving the vehicle, there are three driving behaviors. They are turning left, turning right, and going straight. In order to find out the difference of different driving behaviors on electroencephalogram (EEG) signals, an experimental method was designed to collect the EEG signals from different driving behaviors of drivers. The EEG signals were analyzed and studied by nonlinear dynamics. Firstly, the EEG signals were sub-band decomposed by wavelet transform to extract the alpha wave and the artifacts of the signals were removed. Then, the processed EEG signals from different driving behaviors were calculated to get the complexity and approximate entropy by the Lempel-Ziv complexity algorithm and the approximate entropy algorithm in nonlinear dynamics. The calculation results showed that the complexity value and approximate entropy of the EEG signal from turning left were the largest, and the complexity and approximate entropy of the EEG signal from going straight were the smallest. The result indicated that among the three driving behaviors, the EEG signals of drivers from turning left was relatively more complex and the most random. However, the change rate of the EEG signal complexity and approximate entropy in the three driving behaviors was not large, and the difference was not significant.