Functional brain imaging with M/EEG using structured sparsity in time-frequency dictionaries

Authors:
Alexandre Gramfort;Daniel Strohmeier;Jens Haueisen;Matti Hamalainen;Matthieu Kowalski
Affiliations:
INRIA, Saclay, France and LNAO, NeuroSpin, CEA Saclay, Gif-sur-Yvette Cedex, France and Martinos Center, MGH Dept. of Radiology, Harvard Medical School, Boston, MA;Inst. of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany;Inst. of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany and Biomagnetic Center, Dept. of Neurology, University Hospital Jena, Jena, Germany and Dept. of ...;Martinos Center, MGH Dept. of Radiology, Harvard Medical School, Boston, MA;Laboratoire des Signaux et Systèmes, SUPELEC, Gif-sur-Yvette Cedex, France
Venue:
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Year:
2011

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Abstract

Magnetoencephalography (MEG) and electroencephalography (EEG) allow functional brain imaging with high temporal resolution. While time-frequency analysis is often used in the field, it is not commonly employed in the context of the ill-posed inverse problem that maps the MEG and EEG measurements to the source space in the brain. In this work, we detail how convex structured sparsity can be exploited to achieve a principled and more accurate functional imaging approach. Importantly, time-frequency dictionaries can capture the non-stationary nature of brain signals and state-of-the-art convex optimization procedures based on proximal operators allow the derivation of a fast estimation algorithm. We compare the accuracy of our new method to recently proposed inverse solvers with help of simulations and analysis of real MEG data.