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1 LTCI - Laboratoire Traitement et Communication de l-Information 2 Télécom ParisTech 3 Technische Universität Ilmenau

Abstract : Magnetoencephalography MEG and electroen- cephalography EEG source localization is an ill-posed problem due to a small number of sensors measuring the brain activity. This results in a non-unique source estimate. To identify an appropriate solution out of an infinite set of possible candidates, the problem requires setting certain constraints depending on the assumptions or a priori knowledge about the source distri- bution. Different constraints have been proposed so far, mainly those that impose sparsity on the source reconstruction in both standard and time-frequency domains. Source localization in the time-frequency domain has already been investigated using Gabor dictionary in both a convex TF-MxNE and non-convex way Iterative Reweighted TF-MxNE. The iterative reweighted irTF-MxNE solver has been shown to outperform TF-MxNE in both source recovery and amplitude bias. However, the choice of an optimal dictionary remains unsolved. Due to a mixture of signals, i.e. short transient signals right after the stimulus onset and slower brain waves, the choice of a single dictionary explaining simultaneously both signals types in a sparse way is difficult. In this work, we introduce a method to improve the source estimation relying on a multi-scale dictionary, i.e. multiple dictionaries with different scales concatenated to fit short transients and slow waves at the same time. We compare our results with irTF-MxNE on realistic simulation, then we use somatosensory data to demonstrate the benefits of the approach on in terms of reduced leakage time courses mixture, temporal smoothness and detection of both signals types.

Keywords : Inverse problem MEEG iterative reweighted optimization algorithm multi-scale dictionary Gabor transform.

Autor: Yousra Bekhti - Daniel Strohmeier - Mainak Jas - Roland Badeau - Alexandre Gramfort -

Fuente: https://hal.archives-ouvertes.fr/


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