Adaptive iterative thresholding algorithms for magnetoencephalography (MEG)

Massimo Fornasier, Francesca Pitolli

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

18 Zitate (Scopus)


We provide fast and accurate adaptive algorithms for the spatial resolution of current densities in MEG. We assume that vector components of the current densities possess a sparse expansion with respect to preassigned wavelets. Additionally, different components may also exhibit common sparsity patterns. We model MEG as an inverse problem with joint sparsity constraints, promoting the coupling of non-vanishing components. We show how to compute solutions of the MEG linear inverse problem by iterative thresholded Landweber schemes. The resulting adaptive scheme is fast, robust, and significantly outperforms the classical Tikhonov regularization in resolving sparse current densities. Numerical examples are included.

Seiten (von - bis)386-395
FachzeitschriftJournal of Computational and Applied Mathematics
PublikationsstatusVeröffentlicht - 15 Nov. 2008
Extern publiziertJa


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