Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the help of Bayesian Uncertainty Modelling
This work addresses a domain-specific problem in EEG analysis for medical diagnostics, offering an incremental improvement over existing methods that rely on literature-based conductivity parameters.
The authors tackled the problem of EEG source imaging being sensitive to unknown skull conductivity by proposing a Bayesian approximation error method to simultaneously estimate skull conductivity and reconstruct focal sources, demonstrating improved source localization accuracy and feasible conductivity estimates.
The electroencephalography (EEG) source imaging problem is very sensitive to the electrical modelling of the skull of the patient under examination. Unfortunately, the currently available EEG devices and their embedded software do not take this into account; instead, it is common to use a literature-based skull conductivity parameter. In this paper, we propose a statistical method based on the Bayesian approximation error approach to compensate for source imaging errors due to the unknown skull conductivity and, simultaneously, to compute a low-order estimate for the actual skull conductivity value. By using simulated EEG data that corresponds to focal source activity, we demonstrate the potential of the method to reconstruct the underlying focal sources and low-order errors induced by the unknown skull conductivity. Subsequently, the estimated errors are used to approximate the skull conductivity. The results indicate clear improvements in the source localization accuracy and feasible skull conductivity estimates.