Paper Info
Title
A comparative study of pattern synchronization detection between neural signals using different cross-entropy measures.
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.
Year
DOI
Venue
2010
10.1007/s00422-009-0354-1
Biological Cybernetics
Keywords
Field
DocType
Neural dynamics,Pattern synchronization,Cross-entropy,Nonlinear time series analysis
Cross entropy,Synchronization,Coupling,Computer science,Robustness (computer science),Dynamical systems theory,Artificial intelligence,Stochastic modelling,Bivariate analysis,Machine learning,Electroencephalography
Journal
Volume
Issue
ISSN
102
2
1432-0770
Citations 
PageRank 
References 
8
0.66
6
Authors
3
Name
Order
Citations
PageRank
Hongbo M Xie113712.22
Jingyi Guo229111.92
Yong-Ping Zheng311923.74