Journal
BIOLOGICAL CYBERNETICS
Volume 102, Issue 2, Pages 123-135Publisher
SPRINGER
DOI: 10.1007/s00422-009-0354-1
Keywords
Neural dynamics; Pattern synchronization; Cross-entropy; Nonlinear time series analysis
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Funding
- Hong Kong Research Grant Council [PolyU 5331/06E]
- Hong Kong Polytechnic University [1-BB69]
- Natural Science Foundation of Jiangsu Province [BK2009198]
- Jiangsu University, People's Republic of China [07JDG40]
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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.
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