A high-speed estimation of internal electrical sources in the human brain from the MEG measurements using subspace scanning with multiple scanning resolutions

In neuromagnetism research, it is important to accurately estimate internal electrical source distributions in the human brain from the spatial and temporal measurements of magnetoencephalogram (MEG) activities above the head. In this study, we focused on accelerating distributed source estimation, based on the sub-optimal least-squares subspace scanning technique with multiple scanning resolutions. As a first step, we set a coarse scanning grid over a large area of the head. On the grid points, we calculated the cost function to be used as the criterion for the existence of an internal source. Then, as a second step, we set a fine grid on the area with the largest cost function, and calculated the cost function again. We repeated the above procedure until we got the required resolution. We verified the effectiveness of this method by computer simulation, and applied it to measured MEG data associated with word recognition processes in the human brain. The results showed that the amount of calculation required for the source scanning could be decreased to 1/20 without decreasing the spatial resolution around the source area