Data processing approach for localizing bio-magnetic sources in the brain

Magnetoencephalography (MEG) provides dynamic spatial-temporal insight for neural activities in the cortex. Because the possible number of sources is far greater than the number of MEG detectors, the proposition to localize sources directly from MEG data is ill-posed. Here we develop a novel approach based on a sequence of data processing procedures that includes a clustering process, an intersection analysis, and an application of the maximum entropy method. We examine the performance of our method and compare it with the minimum-norm least-square inverse method using an artificial noisy MEG data.