Nonlinear multivariate analysis of neurophysiological signals

Pereda, Ernesto; Quiroga, Rodrigo Quian and Bhattacharya, Joydeep. 2005. Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology, 77(1-2), pp. 1-37. ISSN 03010082 [Article]

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Abstract or Description

Multivariate time series analysis is extensively used in neurophysiology with the aim of studying the relationship between simultaneously recorded signals. Recently, advances on information theory and nonlinear dynamical systems theory have allowed the study of various types of synchronization from time series. In this work, we first describe the multivariate linear methods most commonly used in neurophysiology and show that they can be extended to assess the existence of nonlinear interdependences between signals.We then review the concepts of entropy and mutual information followed by a detailed description of nonlinear methods based on the concepts of phase synchronization, generalized synchronization and event synchronization. In all cases, we show how to apply these methods to study different kinds of neurophysiological data. Finally, we illustrate the use of multivariate surrogate data test for the assessment of the strength (strong or weak) and the type (linear or nonlinear) of interdependence between neurophysiological signals.

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The article is featured as a cover article and presents a first comprehensive account of nonlinear analysis of multichannel neural signals.

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1 September 2005Published

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10 Dec 2008 10:36

Last Modified:

30 Jun 2017 13:22

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Yes, this version has been peer-reviewed.


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