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Frequency Domain Distributed Inverse Solutions

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Biomag 96

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Abstract

Much interest has been generated in the past few years by Distributed Solutions that estimate the primary current distributions in the whole brain volume. This paper presents a LORETA based distributed method in the Frequency Domain. The method assumes a stochastic stationary time series model for generators that is masked by instrumental noise. Tikhonov regularization is used for dealing with the noise component. A common regularization Parameter is computed by the generalized crossvalidation procedure for the complex valued FFT coefficients. It is shown by simulations that the method is effective for localising generators from noisy data and that a simple LORETA without regularization may fail to recover the generator spectra.

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Author information

Authors and Affiliations

  1. Cuban Neurosciences Center, Havana, Cuba

    R. Casanova, P. Valdes-Sosa, F. M. Garcia, E. Aubert, J. J. Riera & W. Korin

  2. Rotman Research Institute, Toronto, Canada

    O. Lins

Authors
  1. R. Casanova
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  2. P. Valdes-Sosa
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  3. F. M. Garcia
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  4. E. Aubert
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  5. J. J. Riera
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  6. W. Korin
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  7. O. Lins
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Editor information

Editors and Affiliations

  1. Radiology Service Albuquerque, VA Medical Center, Building 49, 114M 1501 San Pedro SE, Albuquerque, NM, 87108, USA

    Cheryl J. Aine

  2. Department of Physics, Dalhousie University, Halifax, Nova Scotia, Canada, B3H 3J5

    Gerhard Stroink

  3. Biophysics Group (P-21), MS D454, Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM, 87545, USA

    Charles C. Wood

  4. Department of New Mexico School of Medicine, Albuquerque, NM, 87131, USA

    Yoshio Okada

  5. Department of Physics and Astronomy, The Open University, Milton Keynes, MK7 6AA, UK

    Stephen J. Swithenby

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© 2000 Springer Science+Business Media New York

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Casanova, R. et al. (2000). Frequency Domain Distributed Inverse Solutions. In: Aine, C.J., Stroink, G., Wood, C.C., Okada, Y., Swithenby, S.J. (eds) Biomag 96. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1260-7_45

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  • DOI: https://doi.org/10.1007/978-1-4612-1260-7_45

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7066-9

  • Online ISBN: 978-1-4612-1260-7

  • eBook Packages: Springer Book Archive

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