Array Response Kernels for EEG/MEG in Single-Shell Ellipsoidal Geometry
David Gutiérrez, Arye Nehorai · 2006
We present analytical forward modeling solutions in the form of array response kernels for electroencephalography (EEG) and magnetoencephalography (MEG) assuming a single-shell ellipsoidal geometry that approximates the anatomy of the head and a dipole current models the source. The structure of our solution facilitates the analysis of the inverse problem by factoring the lead field into a product of the current dipole source and a kernel containing the information corresponding to the head geometry and location of the source and sensors. This factorization allows the inverse problem to be approached as an explicit function of just the location parameters, which reduces the complexity of the estimation solution search. Furthermore, the use of an ellipsoidal geometry is useful for cases when incorporating the anisotropy of the head is important but a better model cannot be defined.