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Author Xie, Hong Bo ♦ Guo, Jing Yi ♦ Zheng, Yong Ping
Source SpringerLink
Content type Text
Publisher Springer-Verlag
File Format PDF
Copyright Year ©2009
Language English
Subject Domain (in DDC) Technology ♦ Medicine & health
Subject Keyword Neural dynamics ♦ Pattern synchronization ♦ Cross-entropy ♦ Nonlinear time series analysis ♦ Statistical Physics, Dynamical Systems and Complexity ♦ Neurobiology ♦ Computer Application in Life Sciences ♦ Neurosciences ♦ Bioinformatics
Abstract Cross-approximate entropy (X-ApEn) and cross-sample entropy (X-SampEn) have been employed as bivariate pattern synchronization measures for characterizing interdependencies between neural signals. In this study, we proposed a new measure, cross-fuzzy entropy (X-FuzzyEn), to describe the synchronicity of patterns. The performances of three statistics were first quantitatively tested using five different coupled systems including both deterministic and stochastic models, i.e., coupled broadband noises, Lorenz–Lorenz, Rossler–Rossler, Rossler–Lorenz, and neural mass model. All the measures were compared with each other with respect to their ability to distinguish between different levels of coupling and their robustness against noise. The three measures were then applied to a real-life problem, pattern synchronization analysis of left and right hemisphere rat electroencephalographic (EEG) signals. Both simulated and real EEG data analysis results showed that the X-FuzzyEn provided an improved evaluation of bivariate series pattern synchronization and could be more conveniently and powerfully applied to different neural dynamical systems contaminated by noise.
ISSN 03401200
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2009-12-23
Publisher Place Berlin/Heidelberg
e-ISSN 14320770
Journal Biological Cybernetics
Volume Number 102
Issue Number 2
Page Count 13
Starting Page 123
Ending Page 135

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Source: SpringerLink