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Multi-modal EEG and fMRI Source Estimation Using Sparse Constraints

  • Saman NoorzadehEmail author
  • Pierre Maurel
  • Thomas Oberlin
  • Rémi Gribonval
  • Christian Barillot
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

In this paper a multi-modal approach is black presented and validated on real data to estimate the brain neuronal sources based on EEG and fMRI. Combining these two modalities can lead to source estimations with high spatio-temporal resolution. The joint method is based on the idea of linear model already presented in the literature where each of the data modalities are first modeled linearly based on the sources. Afterwards, they are integrated in a joint framework which also considers the sparsity of sources. The sources are then estimated with the proximal algorithm. The results are validated on real data and show the efficiency of the joint model compared to the uni-modal ones. We also provide a calibration solution for the system and demonstrate the effect of the parameter values for uni- and multi-modal estimations on 8 subjects.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Saman Noorzadeh
    • 1
    • 2
    Email author
  • Pierre Maurel
    • 1
    • 2
  • Thomas Oberlin
    • 3
  • Rémi Gribonval
    • 1
  • Christian Barillot
    • 1
    • 2
  1. 1.Inria, IRISA CNRS-6074University of Rennes IRennesFrance
  2. 2.Inserm U1228University of Rennes IRennesFrance
  3. 3.IRIT - INP ENSEEIHTUniversity of ToulouseToulouseFrance

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