Nonlinear multivariate analysis of Neurophysiological Signals

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dc.creatorPereda, Ernesto
dc.creatorQuiroga, Rodrigo Quian
dc.creatorBhattacharya, Joydeep
dc.date2005-10-31
dc.date.accessioned2026-07-07T06:48:15Z
dc.date.available2026-07-07T06:48:15Z
dc.descriptionMultivariate 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.
dc.description100 pages (without captions and figures), 12 Figures, To appear in Progress in Neurobiology
dc.identifierhttps://arxiv.org/abs/nlin/0510077
dc.identifierhttp://arxiv.org/abs/nlin/0510077
dc.identifierProgress in Neurobioology, 77/1-2 pp. 1-37 (2005)
dc.identifierdoi:10.1016/j.pneurobio.2005.10.003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/103926
dc.subjectChaotic Dynamics
dc.subjectBiological Physics
dc.subjectData Analysis, Statistics and Probability
dc.subjectNeurons and Cognition
dc.subjectQuantitative Methods
dc.titleNonlinear multivariate analysis of Neurophysiological Signals
dc.typetext

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