forward model at each voxel within the grid, projected against a signal subspace computed for that EEG data. Locations within this grid where the source model gave the best projections onto the signal subspace correspond to the dipole positions. In [54] a conventional two source uniform linear array example was used to compare various versions of MUSIC. A Monte Carlo test was carried out by allowing various runs to find each individual source. For uncorrelated sources all algorithms (MUSIC, S-MUSIC, IES-MUSIC, R-MUSIC and RAP-MUSIC) gave similar results but different performances were then observed as the level of correlation increased. At a correlation coeficient of 0.7, IES-MUSIC and RAP-MUSIC were found to have an RMS error around 25% better than that of MUSIC and S-MUSIC and 50% better than R-MUSIC. As the correlation increases RAP-MUSIC was found to give the best performance but at a value of 0.975 all methods experienced comparable difficulties in estimating the sources. RAP-MUSIC has the added advantage that it provides an automatic way of terminating the search for additional sources when the signal subspace is overestimated [54].
