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Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity.

Ville Rimpiläinen1, Alexandra Koulouri2, Felix Lucka3

  • 1Department of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, D-48149, Münster, Germany.

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|December 12, 2018
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Summary

The Bayesian approximation error (BAE) method improves electroencephalography (EEG) source localization accuracy by accounting for unknown skull conductivity. This approach enhances brain source imaging, especially with inaccurate conductivity models.

Keywords:
Bayesian inverse problemElectroencephalographySkull conductivitySource localizationUncertainty modelling

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Area of Science:

  • Neuroscience
  • Biophysics
  • Medical Imaging

Background:

  • Electroencephalography (EEG) source imaging is crucial for understanding brain activity.
  • Accurate modeling of head tissue conductivity, particularly the skull, is essential but challenging in vivo.
  • Existing methods struggle with the ill-posed nature of EEG inverse problems due to conductivity uncertainties.

Purpose of the Study:

  • To introduce and evaluate the Bayesian approximation error (BAE) approach for EEG source imaging.
  • To demonstrate that precise skull conductivity values are not always necessary for accurate source localization.
  • To quantify the improvements in source localization accuracy using BAE under varying skull conductivity errors and noise levels.

Main Methods:

  • The BAE approach models skull conductivity uncertainty using a probability distribution.
  • An additive error term is incorporated into the observation model to represent conductivity uncertainty.
  • The likelihood is marginalized over this error term before Bayesian inference, focusing on source distribution estimation.

Main Results:

  • BAE significantly improved source localization accuracy, especially when true skull conductivity was lower than expected.
  • Shallow brain sources with initial large localization errors showed the greatest improvement.
  • The benefits of BAE diminished at low signal-to-noise ratios (below 20 dB).

Conclusions:

  • The BAE method offers a robust solution for EEG source imaging by effectively handling skull conductivity uncertainties.
  • This approach enhances the reliability of brain source localization without requiring exact in vivo conductivity measurements.
  • BAE represents a significant advancement in neuroimaging techniques for non-invasive brain activity mapping.