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)


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.


  1. 1.
    Vogel, C.R.: Computational methods for inverse problems, vol. 23. Siam, Philadelphia (2002)CrossRefGoogle Scholar
  2. 2.
    Becker, H., et al.: A performance study of various brain source imaging approaches. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5869–5873. IEEE (2014)Google Scholar
  3. 3.
    Rosa, M., et al.: EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches. J. integr. Neurosci. 9(04), 453–476 (2010)CrossRefGoogle Scholar
  4. 4.
    Karahan, E., et al.: Tensor analysis and fusion of multimodal brain images. Proc. IEEE 103(9), 1531–1559 (2015)CrossRefGoogle Scholar
  5. 5.
    Brookings, T., et al.: Using ICA and realistic bold models to obtain joint EEG/fMRI solutions to the problem of source localization. Neuroimage 44(2), 411–420 (2009)CrossRefGoogle Scholar
  6. 6.
    Bagshaw, A.P., et al.: Analysis of the EEG-fMRI response to prolonged bursts of interictal epileptiform activity. Neuroimage 24(4), 1099–1112 (2005)CrossRefGoogle Scholar
  7. 7.
    Moosmann, M., et al.: Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation. Int. J. Psychophysiol. 67(3), 212–221 (2008)CrossRefGoogle Scholar
  8. 8.
    Deneux, T., et al.: EEG-fMRI fusion of paradigm-free activity using kalman filtering. Neural Comput. 22(4), 906–948 (2010)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Logothetis, N.K., et al.: Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843), 150–157 (2001)CrossRefGoogle Scholar
  10. 10.
    Babajani, A., et al.: Integrated MEG/EEG and fMRI model based on neural masses. IEEE Trans. Biomed. Eng. 53(9), 1794–1801 (2006)CrossRefGoogle Scholar
  11. 11.
    Oberlin, T., et al.: Symmetrical EEG-fMRI imaging by sparse regularization. In: 2015 23rd European Signal Processing Conference on (EUSIPCO), pp. 1870–1874. IEEE (2015)Google Scholar
  12. 12.
    Friston, K.J., et al.: Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. NeuroImage 12(4), 466–477 (2000)CrossRefGoogle Scholar
  13. 13.
    Beck, A., et al.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Combettes, P.L., et al.: Signal recovery by proximal forward-backward splitting. Multiscale Model. Simul. 4(4), 1168–1200 (2005)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Berg, P., et al.: A fast method for forward computation of multiple-shell spherical head models. Electroencephalogr. Clin. Neurophysiol. 90(1), 58–64 (1994)CrossRefGoogle Scholar
  16. 16.
    Albera, L., et al.: Brain source localization using a fourth-order deflation scheme. IEEE Trans. Biomed. Eng. 55(2), 490–501 (2008)CrossRefGoogle Scholar
  17. 17.
    Monti, M.M.: Statistical analysis of fMRI time-series: a critical review of the GLM approach. Front. Human Neurosci. 5(28) (2011)Google Scholar

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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