  The analysis of the interdependence between time series has become an
important field of research in the last years, mainly as a result of advances
in the characterization of dynamical systems from the signals they produce, the
introduction of concepts such as generalized and phase synchronization and the
application of information theory to time series analysis. In neurophysiology,
different analytical tools stemming from these concepts have added to the
'traditional' set of linear methods, which includes the cross-correlation and
the coherency function in the time and frequency domain, respectively, or more
elaborated tools such as Granger Causality. This increase in the number of
approaches to tackle the existence of functional (FC) or effective connectivity
(EC) between two (or among many) neural networks, along with the mathematical
complexity of the corresponding time series analysis tools, makes it desirable
to arrange them into a unified-easy-to-use software package. The goal is to
allow neuroscientists, neurophysiologists and researchers from related fields
to easily access and make use of these analysis methods from a single
integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a
toolbox for the Matlab environment (The Mathworks, Inc), which is designed for
the analysis functional and effective brain connectivity from
neurophysiological data such as multivariate EEG and/or MEG records. It
includes also visualization tools and statistical methods to address the
problem of multiple comparisons. We believe that this toolbox will be very
helpful to all the researchers working in the emerging field of brain
connectivity analysis.

Neurons and Cognition;Computational Engineering, Finance, and Science;Mathematical Software;Biological Physics;Data Analysis, Statistics and Probability