A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information

Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estim...

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Bibliographic Details
Published in:Neural networks Vol. 82; pp. 30 - 38
Main Authors: Cui, Dong, Pu, Weiting, Liu, Jing, Bian, Zhijie, Li, Qiuli, Wang, Lei, Gu, Guanghua
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.10.2016
Subjects:
Aged
Amnestic mild cognitive impairment
Automatic Data Processing - methods
Automatic Data Processing - trends
Brain - physiology
Brain - physiopathology
Cognitive Dysfunction - diagnosis
Cognitive Dysfunction - physiopathology
Cortical Synchronization - physiology
Diabetes Mellitus, Type 2 - diagnosis
Diabetes Mellitus, Type 2 - physiopathology
EEG
Electroencephalography - methods
Electroencephalography - trends
Female
Humans
Male
Mental Processes - physiology
Middle Aged
S-estimator
Synchronization
Type 2 diabetes mellitus
Weighted-permutation mutual information
Weighted-permutation mutual information
Amnestic mild cognitive impairment
Type 2 diabetes mellitus
Synchronization
S-estimator
EEG
ISSN:0893-6080, 1879-2782
Online Access:Get full text
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Summary:Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength.
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ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2016.06.004