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

Content Provider	IEEE Xplore Digital Library
Author	Song, C.Y. Wu, Q. Zhuang, T.G.
Copyright Year	2005
Description	Author affiliation: Dept. of Biomedical Eng., Shanghai Jiao Tong Univ. (Song, C.Y.)
Abstract	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
Starting Page	1079
Ending Page	1082
File Size	237587
Page Count	4
File Format	PDF
ISBN	0780387414
DOI	10.1109/IEMBS.2005.1616606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electroencephalography Inverse problems Current density Image reconstruction Iterative algorithms Focusing Energy resolution Biomedical engineering Space technology Sparse matrices
Content Type	Text
Resource Type	Article

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