HERMES: Towards an integrated toolbox to characterize functional and effective brain connectivity




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Details zur Publikation

Autorenliste: Pereda E
Verlag: Springer (part of Springer Nature): Springer Open Choice Hybrid Journals
Jahr der Veröffentlichung: 2013
Bandnummer: 11
Heftnummer: 4
Erste Seite: 405
Letzte Seite: 434
Seitenumfang: 30
ISSN: 1539-2791
Sprachen: Englisch-Vereinigtes Königreich (EN-GB)


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 (, a
toolbox for the Matlab® environment (The Mathworks, Inc), which is
designed to study 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


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Zuletzt aktualisiert 2019-13-08 um 00:45