Optimising beamformer regions of interest analysis.

Beamforming is a spatial filtering based source reconstruction method for EEG and MEG that allows the estimation of neuronal activity at a particular location within the brain. The computation of the location specific filter depends solely on an estimate of the data covariance matrix and on the forward model. Increasing the number of M/EEG sensors, increases the quantity of data required for accurate covariance matrix estimation. Often however we have a prior hypothesis about the site of, or the signal of interest. Here we show how this prior specification, in combination with optimal estimations of data dimensionality, can give enhanced beamformer performance for relatively short data segments. Specifically we show how temporal (Bayesian Principal Component Analysis) and spatial (lead field projection) methods can be combined to produce improvements in source estimation over and above employing the approaches individually.