Evaluation of sLORETA in the Presence of Noise and Multiple Sources

Abstract

The standardized Low Resolution Brain Electromagnetic Tomography method (sLORETA) can be used to compute statistical maps from EEG and MEG data that indicate the locations of the underlying source processes with low error. These maps are derived by performing a location-wise inverse weighting of the results of a Minimum Norm Least Squares (MNLS) analysis with their estimated variances. In this contribution, we evaluate the performance of the method under the presence of noise and with multiple, simultaneously active sources. It is shown that the sLORETA method localizes well, as compared to other linear approaches such as MNLS and LORETA. However, simultaneously active sources can only be separated if their fields are distinct enough and of similar strength. In the context of a strong or superficial source, weak or deep sources remain invisible, and nearby sources of similar orientation tend not to be separated but interpreted as one source located roughly in between.