IEEE

Talk: Decomposition of dynamical networks with tensor decomposition

Title: Decomposition of dynamical networks with tensor decomposition
Speaker: Dr. Pierre Borgnat (Director of research, LPENSL, CNRS, ENS Lyon, France; Directeur de l’IXXI (Complex Systems Institute Rhône-Alpes))
Venue: Golden Jubilee Seminar Hall, ECE Dept., IISc
Schedule: 04/02/2020, 2.30-3.30 pm
Coffee/tea: 3.30pm
Organized by: Dept. of ECE, IEEE-IISc Student Branch, Signal Processing Society Chapter
Abstract: Dynamical networks are an instance of data that ate studied in many domains, e.g. transportation, social and economical studies, communication networks or biological networks such as brain activity. The range of dynamical features that exist is really wild and many methods have been proposed to extract a reduced number of components, jointly in time and across the (evolving) graph topology. The purpose of the talk is to present some of the works we did in this direction in the context of neuroscience studies, using a novel tensor decomposition approach followed by clustering to extract components representative of various activities in time. The
application context is the study of Functional connectivity (FC) of EEG, which is a graph-like data structure commonly used by neuroscientists to study the dynamic behavior of brain activity. We will show in examples how the proposed approach allows us to decompose data of EEG brain activity of patients suffering from epilepsy, allowing us to infer network components corresponding to the different stages of an epileptic seizure.
Joint work with G. Frusque, P. Gonçalves, J. Jung, R. Cazabet, R. Hamon
Biography: Pierre Borgnat is a CNRS Senior Scientist, at the Laboratory of Physics, ENS de Lyon. Born in France in 1974, he received the Ph.D. degree in Physics and Signal Processing in 2002, and the HDR in 2014. He was a CNRS Chargé de Recherche since 2004, Directeur de Recherche since 2016. He is director of IXXI (Complex System Institute of Rhône-Alpes) since 2014. His research interests are in statistical signal processing, and especially in graph signal processing. He also has interests in complex networks, nonstationary signals or scaling phenomena and machine learning. He works on methodological developments in these domains and studies also several applications of these signal processing methods: Internet traffic modeling and measurements, data processing for physics, analysis of social data, transportation studies and in Neuroscience. He was Associate Editors of IEEE Trans. on Signal Processing (2015-2019) and is currently Area Editor of the same Transactions.
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