Hybrid Weighted Minimum Norm Method A new method based LORETA to solve EEG inverse problem

This paper brings forward a new method to solve EEG inverse problem. Based on following physiological characteristic of neural electrical activity source: first, the neighboring neurons are prone to active synchronously; second, the distribution of source space is sparse; third, the active intensity of the sources are highly centralized, we take these prior knowledge as prerequisite condition to develop the inverse solution of EEG, and not assume other characteristic of inverse solution to realize the most commonly 3D EEG reconstruction map. The proposed algorithm takes advantage of LORETA´s low resolution method which emphasizes particularly on ´localization´ and FOCUSS´s high resolution method which emphasizes particularly on ´separability´. The method is still under the frame of the weighted minimum norm method. The keystone is to construct a weighted matrix which takes reference from the existing smoothness operator, competition mechanism and study algorithm. The basic processing is to obtain an initial solution´s estimation firstly, then construct a new estimation using the initial solution´s information, repeat this process until the solutions under last two estimate processing is keeping unchanged