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UMR 5672

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You are here: Home / Seminars / Colloquium / Decomposition of dynamical networks with tensor decomposition

Decomposition of dynamical networks with tensor decomposition

Pierre Borgnat (CNRS, Laboratoire de Physique & IXXI, ENS de Lyon)
When Feb 10, 2020
from 11:00 to 12:00
Where Amphi. Schrödinger
Attendees Pierre Borgnat
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Dynamical networks are an instance of data that are 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 behaviour 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.

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