activity from the EEG scalp measurements using the lead field and head model, using a three-dimensional solution space restricted to cortical gray matter and electrode coordinates [87]. At each voxel in the cortical grey matter, a 3-component vector time series is computed, corresponding to the current density vector with dipole moments along axes X, Y, and Z [88]. The values in individual voxel reflect the log-transformed fraction of total power across all 6239 voxels that covers the cortical regions of the brain, separately for specific frequencies [59]. While the original LORETA method used weighted minimum norm solution combined depth weighting and Laplacian smoothing, the latest version eLORETA uses optimal weights to achieve localization accuracy [89]. Further technical details of the eLORETA method are available elsewhere [42,88,89]. Inter-subject variability is minimized by the normalized eLORETA solutions, in which current density across voxels are averaged to a unit scale, rendering EEG power density in a Gaussian distribution [90]. In a recent study comparing eLORETA with other inverse methods, Halder et al. [91] found that while all these methods were successful in source identification if false positives were ignored, eLORETA was much superior even when false positives were accounted for. Localization capabilities and
