Virtual depth-electrode measurement using MEG eigenspace beamformer

We have developed a method that can estimate the time course of a neural activity at a predetermined location with a high signal-to-noise ratio. Using this method, an MEG sensor array can perform like a virtual depth electrode in vivo. The developed method consists of two steps: the first step estimating the orientation of a neural source and the second step estimating its activity time course. The second step uses the eigenspace beamformer, which is known to give the signal-to-noise ratio higher than that from the conventional minimum-variance beamformer.