PMID- 27451314
OWN - NLM
STAT- MEDLINE
DCOM- 20170307
LR  - 20181202
IS  - 1879-2782 (Electronic)
IS  - 0893-6080 (Linking)
VI  - 82
DP  - 2016 Oct
TI  - A new EEG synchronization strength analysis method: S-estimator based normalized 
      weighted-permutation mutual information.
PG  - 30-8
LID - S0893-6080(16)30070-3 [pii]
LID - 10.1016/j.neunet.2016.06.004 [doi]
AB  - 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.
CI  - Copyright (c) 2016 Elsevier Ltd. All rights reserved.
FAU - Cui, Dong
AU  - Cui D
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China.
FAU - Pu, Weiting
AU  - Pu W
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China.
FAU - Liu, Jing
AU  - Liu J
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China.
FAU - Bian, Zhijie
AU  - Bian Z
AD  - Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, 
      China.
FAU - Li, Qiuli
AU  - Li Q
AD  - Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, 
      China.
FAU - Wang, Lei
AU  - Wang L
AD  - Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, 
      China.
FAU - Gu, Guanghua
AU  - Gu G
AD  - School of Information Science and Engineering, Yanshan University, Qinhuangdao, 
      China. Electronic address: guguanghua@ysu.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20160705
PL  - United States
TA  - Neural Netw
JT  - Neural networks : the official journal of the International Neural Network 
      Society
JID - 8805018
SB  - IM
MH  - Aged
MH  - Brain/*physiology/physiopathology
MH  - Cognitive Dysfunction/diagnosis/*physiopathology
MH  - Cortical Synchronization/physiology
MH  - Diabetes Mellitus, Type 2/diagnosis/*physiopathology
MH  - Electroencephalography/*methods/trends
MH  - Electronic Data Processing/*methods/trends
MH  - Female
MH  - Humans
MH  - Male
MH  - Mental Processes/physiology
MH  - Middle Aged
OTO - NOTNLM
OT  - Amnestic mild cognitive impairment
OT  - EEG
OT  - S-estimator
OT  - Synchronization
OT  - Type 2 diabetes mellitus
OT  - Weighted-permutation mutual information
EDAT- 2016/07/28 06:00
MHDA- 2017/03/08 06:00
CRDT- 2016/07/25 06:00
PHST- 2015/08/24 00:00 [received]
PHST- 2016/06/17 00:00 [revised]
PHST- 2016/06/21 00:00 [accepted]
PHST- 2016/07/25 06:00 [entrez]
PHST- 2016/07/28 06:00 [pubmed]
PHST- 2017/03/08 06:00 [medline]
AID - S0893-6080(16)30070-3 [pii]
AID - 10.1016/j.neunet.2016.06.004 [doi]
PST - ppublish
SO  - Neural Netw. 2016 Oct;82:30-8. doi: 10.1016/j.neunet.2016.06.004. Epub 2016 Jul 
      5.