title: A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information.
abstract: 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.
authors.affiliation: School of Information Science and Engineering, Yanshan University, Qinhuangdao, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Cui
authors.affiliation: School of Information Science and Engineering, Yanshan University, Qinhuangdao, China.
authors.forename: Weiting
authors.initials: W
authors.lastname: Pu
authors.affiliation: School of Information Science and Engineering, Yanshan University, Qinhuangdao, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, China.
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Bian
authors.affiliation: Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, China.
authors.forename: Qiuli
authors.initials: Q
authors.lastname: Li
authors.affiliation: Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: School of Information Science and Engineering, Yanshan University, Qinhuangdao, China. Electronic address: guguanghua@ysu.edu.cn.
authors.forename: Guanghua
authors.initials: G
authors.lastname: Gu
mesh_terms: D000368:Aged; D001921:Brain / Q000502:physiology* / Q000503:physiopathology; D060825:Cognitive Dysfunction / Q000175:diagnosis / Q000503:physiopathology*; D003344:Cortical Synchronization / Q000502:physiology; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods* / Q000639:trends; D001330:Electronic Data Processing / Q000379:methods* / Q000639:trends; D005260:Female; D006801:Humans; D008297:Male; D008606:Mental Processes / Q000502:physiology; D008875:Middle Aged
keywords: Amnestic mild cognitive impairment; EEG; S-estimator; Synchronization; Type 2 diabetes mellitus; Weighted-permutation mutual information