A hierarchical Krylov–Bayes iterative inverse solver for MEG with physiological preconditioning

DOI10.1088/0266-5611/31/12/125005zbMath1329.35346OpenAlexW2291795049MaRDI QIDQ3462576

Erkki Somersalo, Barbara Vantaggi, Francesca Pitolli, Annalisa Pascarella, Daniela Calvetti

Publication date: 15 January 2016

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/0266-5611/31/12/125005

zbMATH Keywords

prior informationsparsityBayesian hierarchical modelmagnetoencephalographybrain activity

Mathematics Subject Classification ID

Bayesian inference (62F15) Biomedical imaging and signal processing (92C55) Inverse problems for PDEs (35R30)

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Uses Software

• Brainstorm
• MNE

Cites Work

• Unnamed Item
• Regularization methods for large-scale problems
• Preconditioned iterative methods for linear discrete ill-posed problems from a Bayesian inversion perspective
• Bayesian Preconditioned CGLS for Source Separation in MEG Time Series
• The definite non-uniqueness results for deterministic EEG and MEG data
• Bayesian mixture models for source separation in MEG
• Conditionally Gaussian Hypermodels for Cerebral Source Localization
• Priorconditioners for linear systems

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