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.
journal_name
Prog Neurobioljournal_title
Progress in neurobiologyauthors
Pereda E,Quiroga RQ,Bhattacharya Jdoi
10.1016/j.pneurobio.2005.10.003keywords:
subject
Has Abstractpub_date
2005-09-01 00:00:00pages
1-37issue
1-2eissn
0301-0082issn
1873-5118pii
S0301-0082(05)00119-Xjournal_volume
77pub_type
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