A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning
A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning
The inverse problem of MEG aims at estimating electromagnetic cerebral activity from measurements of the magnetic fields outside the head. After formulating the problem within the Bayesian framework, a hierarchical conditionally Gaussian prior model is introduced, including a physiologically inspired prior model that takes into account the preferred directions of …