ABSTRACT
The introduction of Low-resolution electromagnetic tomographic analysis (LORETA) based techniques to neurofeedback introduces several important and unique capabilities. It also places certain new demands on the practical aspects of training. sLORETA is based upon the same basic scientific principles as LORETA, but includes three additional important features. The first is that 6,239 vowels are used. The second is that it incorporates an assumption of smoothness into the solution. The third is that the algorithm has zero localization error. The most important and obvious factor introduced by these approaches is that it is possible to provide feedback related to the activity of a particular region, or regions, of the brain, rather than basing training on scalp activity. LORETA-based techniques can be combined with z-score concepts to provide assessment and training of voxels based on normative or other references. The voxel data are computed in three dimensions, producing a vector for each voxel.
