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Inverse Modeling for MEG/EEG Data

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Mathematical and Theoretical Neuroscience

Part of the book series: Springer INdAM Series ((SINDAMS,volume 24))

Abstract

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.

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Author information

Authors and Affiliations

  1. Dipartimento di Matematica, Università degli Studi di Genova, Via Dodecaneso 35, Genova, Italy

    Alberto Sorrentino & Michele Piana

  2. CNR–SPIN, Genova, Italy

    Alberto Sorrentino & Michele Piana

Authors
  1. Alberto Sorrentino
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  2. Michele Piana
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Correspondence to Alberto Sorrentino .

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Editors and Affiliations

  1. Environment Science and Policy, University of Milan, Milan, Italy

    Giovanni Naldi

  2. Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy

    Thierry Nieus

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Sorrentino, A., Piana, M. (2017). Inverse Modeling for MEG/EEG Data. In: Naldi, G., Nieus, T. (eds) Mathematical and Theoretical Neuroscience. Springer INdAM Series, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-68297-6_15

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