The multiple-signal Classification algorithm (MUSIC) [6,51] is a version of the spatio-temporal approach. The dipole model can consist of fixed orientation dipoles, rotating dipoles or a mixture of both. For the case of a model with fixed orientation dipoles, a signal subspace is first estimated from the data by finding the singular value decomposition (SVD) [8]M = UΣV T and letting U S be the signal subspace spanned by the p first left singular vectors of U. Two other methods of estimating the signal subspace, claimed to be better because they are less affected by spatial covariance in the noise, are given in [52]. The first method involves prewhitening of the data matrix making use of an estimate of the spatial noise covariance matrix. This means that the data matrix M is transformed so that the spatial covariance matrix of the transformed noise matrix is the identity matrix. The second method is based on an eigen decomposition of a matrix product of stochastically independent sweeps. The MUSIC algorithm then scans a single dipole model through the head volume and computes projections onto this subspace. The MUSIC cost function to be minimized is
