Functional Brain Imaging with M/EEG Using Structured Sparsity in Time-Frequency Dictionaries

  • Alexandre Gramfort
  • Daniel Strohmeier
  • Jens Haueisen
  • Matti Hamalainen
  • Matthieu Kowalski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6801)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexandre Gramfort
    • 1
    • 2
    • 3
  • Daniel Strohmeier
    • 4
  • Jens Haueisen
    • 4
    • 5
    • 6
  • Matti Hamalainen
    • 3
  • Matthieu Kowalski
    • 7
  1. 1.INRIA, Parietal teamSaclayFrance
  2. 2.LNAO/NeuroSpin, CEA SaclayGif-sur-Yvette CedexFrance
  3. 3.Martinos Center, MGH Dept. of RadiologyHarvard Medical SchoolBoston
  4. 4.Inst. of Biomedical Engineering and InformaticsIlmenau University of TechnologyIlmenauGermany
  5. 5.Biomagnetic Center, Dept. of NeurologyUniversity Hospital JenaJenaGermany
  6. 6.Dept. of Applied Medical SciencesKing Saud UniversityRiyadhSaudi Arabia
  7. 7.Laboratoire des Signaux et Systèmes (L2S)SUPELECGif-sur-Yvette CedexFrance

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