Realistic forward modeling and source complexity reduction methods for EEG and MEG

With the increasing availability of surface extraction techniques for MRI and x-ray CT images, realistic head models can be readily generated as forward models in the analysis of EEG and MEG data. Inverse analysis of this data, however, requires that the forward model be computationally efficient. We describe two new methods for approximating the EEG forward model using realistic head shapes. The “sensor-fitted sphere” approach fits a multi-layer sphere individually to each sensor, and the “three-dimensional interpolation” scheme interpolates using a grid on which a numerical boundary element method (BEM) technique has been pre-computed. Cast in this framework, high fidelity numerical solutions, currently viewed as computationally prohibitive for solving the inverse problem, can be rapidly recomputed in a highly efficient manner.; We have also developed a new method for generating realistic head models without the use of high resolution spatial data. A realistic head model is constructed via a 3-D thin-plate spline deformation of an anatomically similar surrogate head model. The surrogate head model is warped to a set of landmarks on the subject. We also discuss a simple extension to this method to generate a deformable cortex model for registration of localization solutions to an anatomical surface or atlas. As was the case with the interpolated BEM model, our deformable head model provides whole head coverage.; An important class of experiments in functional brain mapping involve collecting pairs of data corresponding to separate ‘Task’ and ‘Control’ conditions. The data are then analyzed to determine what activity occurs during the Task experiment but not in the Control. We describe a new method for processing paired MEG data sets based on the Recursively Applied and Projected Multiple Signal Classification (RAP-MUSIC) algorithm. In this method the signal subspace of the Task data is projected against the orthogonal complement of the Control data signal subspace to obtain a subspace describing activity unique to the task. Unlike previously published methods, this method is shown to be effective in situations where the time series associated with Control and Task activity possesses significant cross-correlation. This method also allows for straightforward determination of the estimated time series of the localized target sources.