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The inverse problem in electro- and magneto-encephalography (EEG/MEG) aims at reconstructing the underlying current distribution in the human brain using potential differences and/or magnetic fluxes that are measured noninvasively directly, or at a close distance, from the head surface. The simulation of EEG and MEG fields for a given dipolar source in the brain using a volume-conduction model of the head is called the forward problem. The finite element (FE) method, used for the forward problem, is able to realistically model tissue conductivity inhomogeneities and anisotropies, which is crucial for an accurate reconstruction of the current distribution. So far, the computational complexity is quite large when using the necessary high resolution FE models. In this paper we will extend the concept of the EEG lead field basis to the MEG and present algorithms for their efficient computation. Exploiting the fact that the number of sensors is generally much smaller than the number of reasonable dipolar sources, our lead field approach will speed up the state-of-the-art forward approach by a factor of more than 100 for a realistic choice of the number of sensors and sources. Our approaches can be applied to inverse reconstruction algorithms in both continuous and discrete source parameter space for EEG and MEG. In combination with algebraic multigrid solvers, the presented approach leads to a highly efficient solution of FE-based source reconstruction problems.
Wolters et al. (Sat,) studied this question.