Abstract
sLORETA is one of the well-established EEG source localization methods that is popular for its satisfactory estimation, simplicity, and fast computation. However, the method has a low-resolution and requires manual post-processing thresholding to provide a sparser solution with acceptable resolution in source detection. Here we propose a subspace based thresholding that results in a higher resolution brain imaging based on minimizing a desired least square source detection error. Simulation results show the proposed method, denoted by HR-sLORETA, provides stable and high resolution solution in terms of Percentage of Undetected Sources (PUS) and Spatial Dispersion (SD) compared to the existing manual thresholding approaches as well as Otsu thresholding approach. It is shown that HR-sLORETA outperforms Otsu, which is the only other available automatic thresholding method, in scenarios with three or more sources.
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