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

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 electroencephalographic (EEG) and magnetoencephalographic (MEG) data, which rely in dense arrays of detectors in order to measure the brain activity noninvasively. In particular, spatial filters are used in EEG/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 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, whose applicability is shown in this paper for the case when using real MEG measurements and realistic models.