A comparative study of pattern synchronization detection between neural signals using different cross-entropy measures

Hong Bo Xie, Jing Yi Guo, Yongping Zheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)123-135
Number of pages13
JournalBiological Cybernetics
Volume102
Issue number2
DOIs
Publication statusPublished - 1 Feb 2010

Keywords

  • Cross-entropy
  • Neural dynamics
  • Nonlinear time series analysis
  • Pattern synchronization

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science(all)

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