  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 erroneous 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.
