Brain Topography

, Volume 14, Issue 2, pp 131–137 | Cite as

Noninvasive Localization of Electromagnetic Epileptic Activity. I. Method Descriptions and Simulations

  • Rolando Grave de Peralta Menendez
  • Sara Gonzalez Andino
  • Göran Lantz
  • Christoph M. Michel
  • Theodor Landis
Article

Abstract

This paper considers the solution of the bioelectromagnetic inverse problem with particular emphasis on focal compact sources that are likely to arise in epileptic data. Two linear inverse methods are proposed and evaluated in simulations. The first method belongs to the class of distributed inverse solutions, capable of dealing with multiple simultaneously active sources. This solution is based on a Local Auto Regressive Average (LAURA) model. Since no assumption is made about the number of activated sources, this approach can be applied to data with multiple sources. The second method, EPIFOCUS, assumes that there is only a single focal source. However, in contrast to the single dipole model, it allows the source to have a spatial extent beyond a single point and avoids the non-linear optimization process required by dipole fitting. The performance of both methods is evaluated with synthetic data in noisy and noise free conditions. The simulation results demonstrate that LAURA and EPIFOCUS increase the number of sources retrieved with zero dipole localization error and produce lower maximum error and lower average error compared to Minimum Norm, Weighted Minimum Norm and Minimum Laplacian (LORETA). The results show that EPIFOCUS is a robust and powerful tool to localize focal sources. Alternatives to localize data generated by multiple sources are discussed. A companion paper (Lantz et al. 2001, this issue) illustrates the application of LAURA and EPIFOCUS to the analysis of interictal data in epileptic patients.

Focal sources LAURA EPIFOCUS Linear inverse solutions Epilepsy 

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References

  1. Alarcon, G., Guy, C.N., Binnie, C.D., Walker, S.R., Elwes, R.D.C. and Polkey, C.E. Intracerebral propagation of interictal activity in partial epilepsy: implications for sourcelocalisation. J. Neurol. Neurosurg. Psychiatry, 1994, 57: 435-449.PubMedGoogle Scholar
  2. Ary, J.P., Klein, S.A. and Fender, D.H. Location of sources of evoked scalp potentials: corrections for skull and scalp thickness. IEEE Trans. Biomed. Eng. 1981, 28: 447-452.PubMedGoogle Scholar
  3. Ebersole, J.S. EEG dipole modelling in complex partial epilepsy. Brain Topography, 1991, 4: 113-123.PubMedGoogle Scholar
  4. Gonzalez, S.L., Michel C., Lantz, G. and Grave de Peralta Menendez, R. Non stationary distributed source approximation: An alternative to improve localization procedures. Human Brain Mapping, 2001. In Press.Google Scholar
  5. Grave de Peralta, R. and Gonzalez, S.L. Single dipole localization: Some numerical aspects and a practical rejection criterion for the fitted parameters. Brain Topography, 1994, 6(4): 277-282.PubMedGoogle Scholar
  6. Grave de Peralta Menendez, R. and Gonzalez Andino, S.L. A critical analysis of linear inverse solutions. IEEE Trans. Biomed. Eng., 1998, 45: 440-448.PubMedGoogle Scholar
  7. Grave de Peralta Menendez, R. and Gonzalez Andino, S.L. Distributed source models: Standard solutions and new developments. In: C. Uhl (Ed.), Analysis of neurophysiological brain functioning. Springer Verlag, 1999: 176-201.Google Scholar
  8. Grave de Peralta Menendez, R. and Gonzalez, S.L. Discussing the capabilities of laplacian minimization. Brain Topography, 2000, 13(2): 97-104.PubMedGoogle Scholar
  9. Grave de Peralta, R., Gonzalez, S.L., Morand, S., Michel, C.M. and Landis, T. Imaging the electrical activity of the brain: ELECTRA. Human Brain Mapping, 2000, 9: 1-12.PubMedGoogle Scholar
  10. Lantz, G., Ryding, E. and Rosén, I. Three-dimensional localization of interictal epileptiform activity with dipole analysis: comparison with intracranial recordings and SPECT findings. J. Epilepsy, 1994, 7: 117-129.Google Scholar
  11. Lantz, G., Holub, M., Ryding, E. and Rosén, I. Simultaneous intracranial and extracranial recording of interictal epileptiform activity in patients with drug resistant partial epilepsy: patterns of conduction and results from dipole reconstruction. Electroenceph. Clin. Neurophysiol., 1996, 99: 69-78.PubMedGoogle Scholar
  12. Menke, W. Geophysical Data Analysis: Discrete inverse theory. Academic Press, San Diego, California, 1989.Google Scholar
  13. Rao, C.R. and Mitra, S.K. Generalized inverse of matrices and its applications. John Wiley & Sons, Inc., NewYork. 1971.Google Scholar
  14. Ripley, B.D. Spatial Statistics, Wiley, New York, 1981.Google Scholar
  15. Schmidt, R.O. A Signal Subspace Approach to Multiple Emitter Location and Spectral Estimation. Ph.D. thesis, Stanford University, Stanford, California, November 1981.Google Scholar

Copyright information

© Human Sciences Press, Inc. 2001

Authors and Affiliations

  • Rolando Grave de Peralta Menendez
    • 1
  • Sara Gonzalez Andino
    • 1
  • Göran Lantz
    • 1
    • 2
  • Christoph M. Michel
    • 1
    • 2
  • Theodor Landis
    • 1
  1. 1.Functional Brain Mapping Lab., Department of Neurology,University Hospital of Geneva,Switzerland
  2. 2.Plurifaculty Program of Cognitive Neurosciences,University of Geneva,Switzerland

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