Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/12474
Title: Nonlinear multivariate analysis of neurophysiological signals.
Authors: Pereda, E
Quiroga, RQ
Bhattacharya, J
First Published: Sep-2005
Citation: PROG NEUROBIOL, 2005, 77 (1-2), pp. 1-37
Abstract: 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 interdependence 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.
DOI Link: 10.1016/j.pneurobio.2005.10.003
ISSN: 0301-0082
Links: http://hdl.handle.net/2381/12474
Type: Journal Article
Appears in Collections:Published Articles, Dept. of Engineering

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