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

Hong-Bo Xie1, Jing-Yi Guo, Yong-Ping Zheng

  • 1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, People's Republic of China. xiehb@sjtu.org

Biological Cybernetics
|December 25, 2009
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Summary

A new method, cross-fuzzy entropy (X-FuzzyEn), enhances the analysis of pattern synchronization between neural signals. This robust measure offers improved evaluation for bivariate series, even in noisy conditions.

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Area of Science:

  • Computational neuroscience
  • Nonlinear dynamics
  • Biomedical signal processing

Background:

  • Bivariate pattern synchronization measures like cross-approximate entropy (X-ApEn) and cross-sample entropy (X-SampEn) are crucial for analyzing neural signal interdependencies.
  • Existing methods face challenges in distinguishing coupling levels and maintaining robustness against noise.

Purpose of the Study:

  • To introduce a novel bivariate pattern synchronization measure, cross-fuzzy entropy (X-FuzzyEn).
  • To evaluate the performance of X-FuzzyEn against established measures (X-ApEn, X-SampEn) in characterizing neural signal synchronicity.
  • To assess the robustness and applicability of X-FuzzyEn in noisy and complex dynamical systems.

Main Methods:

  • Quantitative performance testing of X-FuzzyEn, X-ApEn, and X-SampEn using five coupled systems: broadband noises, Lorenz-Lorenz, Rossler-Rossler, Rossler-Lorenz, and a neural mass model.
  • Comparison of the measures based on their ability to differentiate coupling levels and their resilience to noise.
  • Application of the measures to analyze pattern synchronization in rat electroencephalographic (EEG) signals from the left and right hemispheres.

Main Results:

  • X-FuzzyEn demonstrated superior performance in evaluating bivariate series pattern synchronization compared to X-ApEn and X-SampEn.
  • The proposed X-FuzzyEn exhibited enhanced robustness against noise.
  • Both simulated and real EEG data analyses confirmed the advantages of X-FuzzyEn.

Conclusions:

  • Cross-fuzzy entropy (X-FuzzyEn) offers an improved and more powerful tool for assessing pattern synchronization in bivariate time series.
  • X-FuzzyEn is particularly advantageous for analyzing neural dynamical systems, especially when contaminated by noise.
  • The method shows promise for broader applications in complex systems analysis.