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

• 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
• DOI: 10.1016/j.neunet.2016.06.004

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.