MEG source estimation in the presence of low-rank interference using cross-spectral metrics

We estimate a source current dipole at a known location in the presence of low-rank interference using magnetoencephalography (MEG). We present a space-time processor for MEG data based on the generalized sidelobe canceler (GSC). We extend the classical vector beamformer to a matrix structure without making any assumptions on the rank of the covariance matrix of noise and interference, or constraint matrices. Furthermore, we define the cross-spectral metrics (CSM) in their most general form. The CSM method is known to approximate the performance of the matched filter for the case of unknown covariance matrix. In our case, the CSM also allows to reduce the complexity of the filtering problem without significant loss of performance in the signal-to-interference-plus-noise ratio (SINR). Our results show that good estimates of the dipole sources can be achieved by only using a few eigenvalues, namely, those corresponding to the largest CSM.