How reducing model mismatch is beneficial to EEG source localization: Simulation study

Forward modeling errors as well as measurement noise at sensor surface are propagated into errors in EEG source localization. In order to reduce forward modeling errors, tremendous effort has gone in to estimate real head models as accurately as possible, thereby, yielding more accurate source localization. Additionally, de-noising approaches have been developed to reduce the effect of noise. However, both noise and model mismatch are essentially unavoidable. Typically, the noise level depends on the EEG data to be analyzed. Unaveraged data has a substantially higher noise level than averaged data. For the given noise level of EEG data, how is reducing model mismatch beneficial to EEG source localization? In this work, we attempt to answer this question through an intensive simulation study. Three-shell (representing scalp, skull and brain) concentric spherical head models, with meshes of different fineness are generated. Assuming that the finest mesh model has no modeling errors, about 60,000 single dipole problems are generated on it. Then they are localized on several coarser models using the beamforming technique. Homogeneous conductivity values are assigned for each shell and the finite element method (FEM) is applied for forward computation. Finally, averaged localization error distribution is obtained over signal-to-noise ratios and over different mesh models to see the modeling error effects. It is found that reducing modeling errors has substantial gain in localization, but the gain is marginal after the modeling error is less than a particular value.