Neuroelectric source localization by random spatial sampling

The magnetoencephalography (MEG) aims at reconstructing the unknown neuroelectric activity in the brain from the measurements of the neuromagnetic field in the outer space. The localization of neuroelectric sources from MEG data results in an ill-posed and ill-conditioned inverse problem that requir...

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Bibliographic Details
Published in:Journal of computational and applied mathematics 2016-04, Vol.296, p.237-246
Main Authors: Pitolli, F., Pocci, C.
Format: Article
Language:English
Subjects:
Brain
Inverse problem
Inverse problems
Localization
Magnetoencephalography
Mathematical models
Neuroimaging
Position (location)
Random sampling
Regularization
Sampling
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Description
Summary:The magnetoencephalography (MEG) aims at reconstructing the unknown neuroelectric activity in the brain from the measurements of the neuromagnetic field in the outer space. The localization of neuroelectric sources from MEG data results in an ill-posed and ill-conditioned inverse problem that requires regularization techniques to be solved. In this paper we propose a new inversion method based on random spatial sampling that is suitable to localize focal neuroelectric sources. The method is fast, efficient and requires little memory storage. Moreover, the numerical tests show that the random sampling method has a high spatial resolution even in the case of deep source localization from noisy magnetic data.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2015.09.028