eprintid: 21459 rev_number: 21 eprint_status: archive userid: 1903 dir: disk0/00/02/14/59 datestamp: 2013-11-05 19:03:10 lastmod: 2014-11-01 23:56:12 status_changed: 2013-11-05 19:03:10 type: article metadata_visibility: show item_issues_count: 0 creators_name: Niso Galán, Julia Guiomar creators_name: Bruña Fernandez, Ricardo creators_name: Pereda, Ernesto creators_name: Gutiérrez Díez, Ricardo creators_name: Bajo Breton, Ricardo creators_name: Maestú, Fernando creators_name: Pozo Guerrero, Francisco del title: HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity ispublished: pub subjects: psicologia abstract: 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 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. date: 2013-10 date_type: published official_url: http://link.springer.com/article/10.1007%2Fs12021-013-9186-1 id_number: 10.1007/s12021-013-9186-1 full_text_status: public publication: Neuroinformatics volume: 11 number: 4 pagerange: 405-434 institution: Tecnologia_Biomedica department: otro refereed: TRUE issn: 1539-2791 rights: by-nc-nd citation: Niso Galán, Julia Guiomar, Bruña Fernandez, Ricardo, Pereda, Ernesto, Gutiérrez Díez, Ricardo, Bajo Breton, Ricardo, Maestú, Fernando and Pozo Guerrero, Francisco del (2013). HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity. "Neuroinformatics", v. 11 (n. 4); pp. 405-434. ISSN 1539-2791. https://doi.org/10.1007/s12021-013-9186-1 . document_url: https://oa.upm.es/21459/1/INVE_MEM_2013_144984.pdf