New HERMES workshop in Murcia at SEPNECA 2014
New HERMES workshop at SEPNECA conference in Murcia on 1st October. See details here.
UPDATE: The HERMES version and the data sets that will be used in the SEPNECA workshop are already avaliable here.
The easy way to estimate connectivity
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, and the introduction of concepts such as Generalized (GS) and Phase synchronization (PS) and information theory as applied to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the “traditional” set of linear methods, which include the cross-correlation function (in the time domain), the coherence function (in the frequency domain) 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 toolbox, which integrates all the methods as well as related preprocessing procedures.
Here, we present a Matlab® toolbox, called HERMES, which includes several commonly used linear and nonlinear indexes of FC and EC, ranging from the traditional cross-correlation and coherence functions to advanced measures of interdependence based on information theory such as transfer entropy. HERMES has been specially (but not exclusively) designed for the analysis of neurophysiological data from multivariate EEG and MEG records, and it includes also visualization tools and statistical methods that deal with 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.
The authors acknowledge the financial support of:
- The Ministry of Education of the Community of Madrid through R&D program NEUROTEC-CM (S2010/BMD-2460)
- The Spanish Ministry of Economy and Competitiveness through grants TEC2012-38453-CO4-01 and -03.