Robust MEG Source Localization of Event Related Potentials: Identifying Relevant Sources by Non-Gaussianity

  • Peter Breun
  • Moritz Grosse-Wentrup
  • Wolfgang Utschick
  • Martin Buss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


Independent Component Analysis (ICA) is a frequently used preprocessing step in source localization of MEG and EEG data. By decomposing the measured data into maximally independent components (ICs), estimates of the time course and the topographies of neural sources are obtained. In this paper, we show that when using estimated source topographies for localization, correlations between neural sources introduce an error into the obtained source locations. This error can be avoided by reprojecting ICs onto the observation space, but requires the identification of relevant ICs. For Event Related Potentials (ERPs), we identify relevant ICs by estimating their non-Gaussianity. The efficacy of the approach is tested on auditory evoked potentials (AEPs) recorded by MEG. It is shown that ten trials are sufficient for reconstructing all important characteristics of the AEP, and source localization of the reconstructed ERP yields the same focus of activity as the average of 250 trials.


Source Localization Independent Component Analysis Event Relate Potential Independent Component Analysis Blind Source Separation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baillet, S., Mosher, J.C., Leahy, R.M.: Electromagnetic brain mapping. IEEE Signal Processing Magazine 18(6), 14–30 (2001)CrossRefGoogle Scholar
  2. 2.
    Comon, P.: Independent component analysis, a new concept? Signal Processing 36(3), 287–314 (1994)MATHCrossRefGoogle Scholar
  3. 3.
    Cao, J., Murata, N., Amari, S., Cichocki, A., Takeda, T.: Independent component analysis for unaveraged single-trial MEG data decomposition and single-dipole source localization. Neurocomputing 49, 255–277 (2002)CrossRefGoogle Scholar
  4. 4.
    Zhukov, L., Weinstein, D., Johnson, C.: Independent component analysis for EEG source localization - an algorithm that reduces the complexity of localizing multiple neural sources. IEEE Engineering in Medicine and Biology Magazine 19(3), 87–96 (2000)CrossRefGoogle Scholar
  5. 5.
    Mosher, J.C., Lewis, P.S., Leahy, R.M.: Multiple Dipole Modeling and Localization from Spatio-Temporal MEG Data. IEEE Transactions on Biomedical Engineering 39(6), 541–557 (1992)CrossRefGoogle Scholar
  6. 6.
    Hämäläinen, M.S., Ilmoniemi, R.J.: Interpreting magnetic fields of the brain: minimum norm estimates. Medical & Biological Engineering & Computing 32, 35–42 (1994)CrossRefGoogle Scholar
  7. 7.
    Pascual-Marqui, R.D., Michel, C.M., Lehmann, D.: Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology 18, 49–65 (1994)CrossRefGoogle Scholar
  8. 8.
    Gorodnitsky, I.F., George, J.S., Rao, B.D.: Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. Journal on Electroencephalography and clinical Neurophysiology 95(4), 231–251 (1995)CrossRefGoogle Scholar
  9. 9.
    Cotter, S.F., Rao, B.D., Engan, K., Kreutz-Delgado, K.: Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors. IEEE Transactions on Signal Processing 53(7), 2477–2488 (2005)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing 20(1), 33–61 (1998)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Makeig, S., Bell, A.J., Jung, T.P., Sejnowski, T.J.: Independent Component Analysis of Electroencephalographic Data. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems 8, pp. 145–151. MIT Press, Cambridge (1996)Google Scholar
  12. 12.
    Grosse-Wentrup, M., Buss, M.: Subspace Identification Through Blind Source Separation. IEEE Signal Processing Letters 13(2), 100–103 (2006)CrossRefGoogle Scholar
  13. 13.
    Joho, M., Mathis, H., Lambert, R.H.: Overdetermined blind source separation: Using more sensors than source signals in a noisy mixture. In: Proc. Independent Component Analysis and Blind Signal Separation ICA, pp. 81–86 (2000)Google Scholar
  14. 14.
    Bell, A.J., Sejnowski, T.J.: An information maximization approach to blind separation and blind deconvolution. Neural Computation 7(6), 1129–1159 (1995)CrossRefGoogle Scholar
  15. 15.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods 134, 9–21 (2004)CrossRefGoogle Scholar
  16. 16.
    Dalal, S.S., Sekihara, K., Nagarajan, S.S.: Modified Beamformers for Coherent Source Region Suppression. IEEE Transactions on Biomedical Engineering (2006) (accepted for future publication)Google Scholar
  17. 17.
    Stephens, M.A.: EDF Statistics for Goodness of Fit and Some Comparisons. Journal of the American Statistical Association 69(347), 730–737 (1974)CrossRefGoogle Scholar
  18. 18.
    Nagarajan, S.S., Attias, H.T., Hild, I.K.E., Sekihara, K.: A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity. Neuroimage 30(2), 400–416 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Breun
    • 1
  • Moritz Grosse-Wentrup
    • 2
  • Wolfgang Utschick
    • 1
  • Martin Buss
    • 2
  1. 1.Institute for Circuit Theory and Signal ProcessingTechnische Universität MünchenMünchenGermany
  2. 2.Institute of Automatic Control Engineering (LSR)Technische Universität MünchenMünchenGermany

Personalised recommendations