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

Ville Rimpiläinen, Alexandra Koulouri, Felix Lucka, Jari P. Kaipio, Carsten H. Wolters

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Abstract

Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.

Original languageEnglish
Pages (from-to)252-260
Number of pages9
JournalNeuroImage
Volume188
Early online date6 Dec 2018
DOIs
Publication statusPublished - 1 Mar 2019

Funding

This work was supported in parts by the Finnish Cultural Foundation ( 00140811 ), the Academy of Finland post-doctoral program (project no. 316542 ), IKY Fellowships of excellence for postgraduate studies in Greece - Siemens program , the Engineering and Physical Sciences Research Council, UK ( EP/K009745/1 ), the European Union’s Horizon 2020 research and innovation programme H2020 ICT 2016-2017 under grant agreement No 732411 (as an initiative of the Photonics Public Private Partnership) and the Netherlands Organisation for Scientific Research ( NWO 613.009.106/2383 ), EU project ChildBrain (Marie Curie Innovative Training Networks, grant agreement no. 641652 ) and by the Deutsche Forschungsgemeinschaft (DFG, project WO1425/7-1 ). Appendix

Keywords

  • Bayesian inverse problem
  • Electroencephalography
  • Skull conductivity
  • Source localization
  • Uncertainty modelling

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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