Adaptive iterative thresholding algorithms for magnetoencephalography (MEG)

Massimo Fornasier, Francesca Pitolli

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish
Pages (from-to)386-395
Number of pages10
JournalJournal of Computational and Applied Mathematics
Issue number2
StatePublished - 15 Nov 2008
Externally publishedYes


  • Adaptive algorithms
  • Inverse problems
  • Iterative thresholding
  • Magnetoencephalography
  • Matrix compression
  • Wavelets


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