Bayesian Tracking of Neural Activity in Biomagnetic Data

Autori

  • Cristina Campi Dipartimento di Matematica Universita' di Genova, CNR-INFM LAMIA
  • Annalisa Pascarella Dipartimento di Informatica Universita' di Verona, CNR-INFM LAMIA
  • Alberto Sorrentino CNR-INFM LAMIA
  • Michele Piana Dipartimento di Informatica Universita' di Verona, CNR-INFM LAMIA

DOI:

https://doi.org/10.1685/

Parole chiave:

inverse problem, Bayesian tracking, magnetoencephalography.

Abstract

Magnetoencephalography (MEG) is a non-invasive brain imaging tecnique measuring the weak magnetic field due to neural activity. The analysis of the temporal evolution of the magnetic field, however, does not provide accurate spatial information about the neural activations in the cerebral cortex. Such information can be restored only by solving the inverse problem. We propose a probabilistic approach to solve this problem: a particle filter is implemented to realize a Bayesian tracking of the brain sources, modeled as pointwise currents. [DOI: 10.1685/CSC09258] About DOI

Dowloads

Pubblicato

2009-08-12

Fascicolo

V. 3 (2009)

Sezione

Articles

Licenza

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