The Application Of Chaotic Time Series Analysis In EEG Signal Processing

EEG signals collected from cerebral cortex by electrodes contain abundant information about cranial nervous activities. Because EEG synthetically characterizes the functions and states of cerebral activities, it has become a mainstream to analyse EEG with all kinds of methods. The characteristic parameters of nonlinear dynamical analysis of EEG time series and the relationship between those parameters and cerebracal states or diseases is an important direction in EEG signal processing.In this paper, based on the chaotic time series analysis and the investigation on EEG research at home and abroad, I worked out the nonlinear dynamical characteristic parameters and their analysis of EEG that was recorded when the point ST36 of leg was stimulated.EEG used in this research was recorded in our lab. It is easy to be disturbed by noise, but the wavelet transform has good ability for unstable signals both in time and frequency area, so the main power information of EEG could be extracted from the original signal by wavelet decomposition. Based on the investigation on nonlinear dynamics of EEG research at home and abroad, I used the dot plot and surrogate data to confirm that EEG has the chaotic character. I also used two methods to reconstruct the phase space, and choosed the right reconstructed parameters for EEG time series analysis. I used the improved G-P arithmetic and small data method to achieve the correlation dimension and largest Lyapunov exponent. As chaotic measures of EEG, approximate entropy and recurrence plot possess many merits, such as small calculation capacity, simple algorithm and strong antijamming, they are also good parameters to distinguish the different states and samples. For the algorithmic realization, I combined the advantage of MATLAB and C language, as it enhanced the rate of data processing greatly. As for the stimulation of the point ST36 of left leg, I calculated the values of correlation dimension, largest Lyapunov exponent, approximate entropy, recurrence rate and determinate rate. The comparison of the eigenvalues gained before, during and after stimulation shows that the eigenvalues of the right side brain's EEG increased evidently during the stimulation, especially in the frontal and temporal lobes. It indicates that this part of the brain have an active response during the stimulation, and may provide the theory for the research of the recognition ability.