2013/05/27 by Guiomar Niso, Ricardo Bruña, Ernesto Pereda +4
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · Physics and Astronomy · #EEG and Brain-Computer Interfaces #Functional Brain Connectivity Studies #Neural dynamics and brain function #cs.CE #cs.MS #physics.bio-ph #physics.data-an #q-bio.NC
paper · pdf · doi:10.1007/s12021-013-9186-1
58 pages, 10 figures, 3 tables, Neuroinformatics 2013
arxiv created 2013/05/27 · arxiv updated 2013/05/28 · openalex publication_date 2013/06/27 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.