  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.
