NeuroImage | 2019
Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity
Abstract
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 20dB. HIGHLIGHTSBayesian uncertainty modelling was used for the unknown skull conductivity in EEG.Modelling error statistics were pre‐computed before the source analysis.Source localization accuracy improved particularly for the sources close to the skull.Computational complexity was essentially the same as in the plain source analysis.Bayesian uncertainty modelling is easily adjustable to include other modelling errors.