EEG forward problem numerical solvers analysis for the FDM technique

We investigate the use of different preconditioning within the framework of the Anisotropic-Finite-Difference based Solution for the EEG Forward Problem. Provided the minimal error of representation, comparison of the convergence rate and computational cost is carried out for several competitive numerical solver combinations. From the testing on real data, we obtain that combination of the biconjugate gradient solver and incomplete LU factorization results in a numerical solution that outperforms the other considered approaches in terms of reducing the computational cost significantly. We validate this numerical solution combination against analytical spherical mode. Also, testing on realistic head models (with high anisotropic areas and heterogeneous tissue conductivities) shows high accuracy and low computational cost.