# Bayesian Modelling of Skull Conductivity Uncertainties in EEG Source Imaging

## TL;DR

This paper introduces a Bayesian statistical method to improve EEG source imaging accuracy by compensating for uncertainties in skull conductivity, which varies between individuals and is hard to measure.

## Contribution

It presents a novel Bayesian approximation error approach to address skull conductivity uncertainties in EEG source imaging.

## Key findings

- Significant improvement when sources are near the skull
- Effective compensation for skull conductivity errors
- Potential for personalized EEG source imaging

## Abstract

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.