Non-linear Dynamic Methods Researching EEG Signals Based On Recurrence Plot

Mild cognitive impairment is an intermediate state between normal aging and dementia.It has a high chance of developing dementia,which seriously affects the quality of life of patients and causes great distress to human beings.The brain is the most advanced part of the nervous system.EEG signals contain a large amount of human physiology and pathological information.The extraction of EEG features and their quantitative study are of great significance for the prevention,diagnosis,and treatment of neurological diseases.In this paper,we study the nonlinear dynamics of EEG signals in patients with mild cognitive impairment of diabetes by studying multiscale recurrence plot and cross recurrence plot.First,a simple analysis of EEG signals,including the generation mechanism,acquisition,zoning,characteristics and classification of EEG signals,introduces several EEG signal preprocessing methods.A detailed introduction and analysis of the neurological psychological scales evaluated by MCI at home and abroad were made.The inclusion and exclusion criteria for the MCI group and the control group were established.Second,using the Logistic model for the order recurrence plot algorithm for simulation analysis,it is found that this algorithm has good anti-noise performance and does not require consider data length.Analyze the change of deterministic variable DET and its anti-noise performance of different sorts of recursive maps.This algorithm analyzes the actual EEG signals of the MCI group and the control group and finds that the MCI group has a higher DET value than the control group,and most of the electrodes have significant differences at a small scale.It was Pearson's linear correlation analysis with neuropsychological tests and the results showed that there was a correlation with cognitive function.Finally,the cross recurrence plot method is simulated and analyzed.The MIX(p)model is used to analyze the coupling coefficient,anti-noise performance and data length.It is found that this algorithm has a certain anti-noise ability and not consider data length.It was used to analyze the actual brain electrical signals in the MCI group and the control group.It was found that the DET value of the cross recurrence plot of the MCI group was higher than that of the control group,and there was a sign ificant difference in some electrode pairs.It was Pearson's linear correlation analysis with neuropsychological tests and the results showed that there was a correlation with cognitive function.