New Perspectives on the Use of Spatial Filters in Magnetoencephalographic Array Processing

Abstract

The increase in computer power of the last few decades has allowed the resurgence of the theory behind spatial filtering (a.k.a. beamforming) and its application to array signal processing. That is the case of magnetoencephalographic (MEG) data, which relies on dense arrays of detectors in order to measure the brain activity non-invasively. In particular, spatial filters are used in MEG signal processing to estimate the magnitude and location of the current sources within the brain. This is achieved by calculating different beamformer-based indexes which usually involve a large computational complexity. Here, a new perspective on how today’s computers make it possible to handle such complexity is presented, up to the point when new and ever more complex neural activity indexes can be developed. Such is the case of indexes based on eigenspace projections and reduced-rank beamformers, whose applicability is shown in this paper for the case of using real MEG measurements and realistic models.