Frequency-spatial beamformer for MEG source localization

Abstract

This article introduces a unique and powerful new method for spatial localization of neuronal sources that exploit the high temporal resolution of magnetoencephalography (MEG) data to locate the originating sources within the brain. A traditional frequency beamforming algorithm was adapted from its conventional application to yield information on the spatial location of simulated neuronal signals. The concept is similar to that used in signal source localization in magnetic resonance imaging (MRI) in which spatial location is determined by the frequency of oscillation of the MR signal. Whereas a traditional frequency beamformer uses the time course values of all sensors in the dataset to assign a power value for each possible frequency in the signal, it provides no information on the spatial location of those frequencies. Our approach assigns a power value to each location in the three-dimensional head volume. To compute this power value, the time courses of a subset of sensors closest to that location in space are used rather than all the time courses in the dataset. Our novel technique incorporates actual MEG sensor locations of the closest sensors at each location in space. The approach is relatively simple to implement, yields good spatial resolution, and accurately spatially locates a simulated source in low signal-to-noise environments. In this work, its performance is compared to that of the synthetic aperture magnetometry (SAM) beamformer and shown to exhibit improved spatial resolution.