An alternative subspace approach to EEG 3-D dipole source localization

In the present study, we investigate a new approach to EEG 3-D dipole source localization by adapting a non-recursive subspace algorithm called FINES. In estimating dipole locations, the present approach employs projections onto a particular vector set in the estimated noise-only subspace instead of the entire estimated noise-only subspace that MUSIC is based on. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular location region. By incorporating knowledge of the array manifold in identifying different vector sets in the estimated noise-only subspace for different location regions, this approach is able to enhance dipole source resolvability and reduce estimation errors. The present computer simulations show in EEG 3-D dipole source localization that FINES has 1) better resolvability of two closely-spaced sources, and 2) better estimation accuracy of source locations, compared to classic MUSIC.