The Research Of Multi-channel EEG Synchronization Algorithm Based On The Weighted Entropy

The synchronization of Electroencephalogram(EEG) oscillation is a distinct feature of information transmitting, processing and functional integration among different brain areas. The multichannel EEG synchronization analysis method can help us recognize the function of brain, to explore the mechanism of the information processing in different brain areas, in-depth understanding of the mechanism of the brain disorder that leads to the disease. That is important to the diagnosis, prevention and treatment in brain nerve disease. This paper studied the multi-channel EEG synchronization algorithm, proposed the S-estimator based on Weight Normalization Permutation Mutual Information(SWNPMI), and used this method to analyze the actual EEGs collected from diabetic with amnestic mild cognitive impairment.Firstly, the three of multi-channel synchronous analysis method, such as S estimator, synchronization index, global synchronization index, had been analyzed by model simulation in the width of window, signal to noise ratio and the coupling coefficient. These three methods had also been used to analyze the synchronization on the actual diabetic brain source EEGs. The statistical analysis in amnestic mild cognitive impairment(a MCI) groups and control groups of diabetes showed that, these two groups had significant difference in the delta and beta2 bands of parietal and temporal regions; it also had significant difference in the theta and beta2 bands of occipital region.Secondly, based on the weighted permutation entropy and mutual information, we proposed the double channel EEG synchronization algorithm that is Weight Normalization Permutation Mutual Information(WNPMI). From the perspective of coupling coefficient, data length, delay time, embedding dimension and noise, the WNPMI was compared with the Normalization Permutation Mutual Information(NPMI) and the conclusion showed that the WNPMI performed much better than the NPMI. Therefore, based on the WNPMI and the S estimator, one multichannel EEG synchronization algorithm had been proposed, which called SWNPMI. This method is very suitable for multi-channel EEG synchronization analysis, and has good reliability and robustness.Finally, S-estimator based on Normalization Permutation Mutual Information(SNPMI) and the SWNPMI were applied to analysis the EEGs that collected from the patients with diabetes. The statistical analysis found that there are significant differences between the a MCI group and control group with SWNPMI in Postfrontal regions, Central regions and Occipital regions. Pearson linear correlation analysis in the synchronization and neuropsychological test scores for all patients, as a result, there has an association between cognitive function and the synchronization of EEGs.