Statistical Mapping of Cortical Activities using Minimum-Variance Maximum-Discrimination Spatial Filtering

This paper presents a new spatial filtering technique for statistical mapping of neuronal sources by using magnetoencephalography data. In addition to the unit-gain constraint and the minimum-variance criterion that can reconstruct the activation magnitude of the targeted source while suppressing the contribution from other sources, the proposed technique exploits a maximum-discrimination criterion that can maximize the discrepancy between the reconstructed neuronal activities in the control (or resting) state and those in the active state. Imposing the maximumdiscrimination criterion leads to a closed-form solution of the source orientation, which can then be used to compute the proposed minimum-variance maximum-discrimination spatial filter for each probed position. When applied to a finger-lifting study, F-statistic map computed from the reconstructed neuronal activities on the cortical surface clearly identify the sensorimotor area with high contrast. Keywords—Spatial filter, maximum discrimination, neuromagnetic imaging, beamformer, synthetic aperture magnetometry