Bayesian Modelling of Skull Conductivity Uncertainties in EEG Source Imaging

  • Ville Rimpiläinen
  • Alexandra Koulouri
  • Felix Lucka
  • Jari P. Kaipio
  • Carsten H. Wolters
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 65)


Knowing the correct skull conductivity is crucial for the accuracy of EEG source imaging, but unfortunately, its true value, which is inter- and intra-individually varying, is difficult to determine. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors related to erronous skull conductivity. We demonstrate the potential of the approach by simulating EEG data of focal source activity and using the dipole scan algorithm and a sparsity promoting prior to reconstruct the underlying sources. The results suggest that the greatest improvements with the proposed method can be achieved when the focal sources are close to the skull.


Electroencephalography Bayesian modelling inverse problems skull conductivity 


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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ville Rimpiläinen
    • 1
  • Alexandra Koulouri
    • 2
  • Felix Lucka
    • 3
  • Jari P. Kaipio
    • 4
  • Carsten H. Wolters
    • 1
  1. 1.Institute for Biomagnetism and BiosignalanalysisUniversity of MünsterMünsterGermany
  2. 2.Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
  3. 3.Centre for Medical Image ComputingUniversity College LondonLondonUK
  4. 4.Department of MathematicsUniversity of AucklandAucklandNew Zealand

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