Linearly constrained minimum variance source imaging using cortical bases.

Abstract

An approach is presented for representing spatially extended cortical activity using a basis function expansion. The bases are designed to represent patches on the cortical surface. The basis function expansion coefficients are estimated for each patch by scanning modified linearly constrained minimum variance (LCMV) spatial filters over the entire surface. Next, a generalized likelihood ratio test (GLRT) is performed to detect patches with significant activity. In the last step, an image of the activity within each patch is reconstructed using a minimum norm solution to a local inverse problem. We show that the basis function representation enables the LCMV approach to identify patches of coherent activity that are missed by the conventional LCMV method and has potential for extended source detection and localization.