Bayesian multi–dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings

  • Filippo Rossi
  • Gianvittorio Luria
  • Sara Sommariva
  • Alberto Sorrentino
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 65)


We deal with estimation of multiple dipoles from combined MEG and EEG time–series. We use a sequential Monte Carlo algorithm to characterize the posterior distribution of the number of dipoles and their locations. By considering three test cases, we show that using the combined data the method can localize sources that are not easily (or not at all) visible with either of the two individual data alone. In addition, the posterior distribution from combined data exhibits a lower variance, i.e. lower uncertainty, than the posterior from single device.


Bayesian method sequential Monte Carlo dipole modeling magnetoencephalography electroencephalography 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Filippo Rossi
    • 1
  • Gianvittorio Luria
    • 1
  • Sara Sommariva
    • 1
  • Alberto Sorrentino
    • 1
  1. 1.Dipartimento di MatematicaUniversità degli Studi di GenovaGenovaItaly

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