SWARM: sLORETA-weighted accurate minimum norm inverse solutions

Standardized Low Resolution Electromagnetic Tomography (sLORETA) localizes the sources of EEG and MEG data with low error. Its outcome, however, is not a distribution of currents modelling brain activity but a statistical map thereof. In this paper, a new inverse method for current density reconstructions from EEG and MEG data is presented. The sLORETA-weighted accurate minimum norm method (SWARM) belongs to the family of weighted minimum norm solutions with its weights based on sLORETA. SWARM localizes point sources with low error, but, unlike sLORETA, it computes a current density vector field. The method is described and its performance is demonstrated for EEG and MEG dipole simulations at different depths.