A two-way regularization method for MEG source reconstruction

DOI10.1214/11-AOAS531zbMath1254.92059arXiv1209.6443OpenAlexW3098382855MaRDI QIDQ714359

Haipeng Shen, Tian Siva Tian, Jianhua Z. Huang, Zhi-Min Li

Publication date: 21 October 2012

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1209.6443

zbMATH Keywords

inverse problemspatio-temporaltwo-way regularization

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Neural biology (92C20) Biomedical imaging and signal processing (92C55) Software, source code, etc. for problems pertaining to biology (92-04)

Related Items (8)

Multi-dimensional functional principal component analysis ⋮ Estimating a common period for a set of irregularly sampled functions with applications to periodic variable star data ⋮ Scalable spatio‐temporal Bayesian analysis of high‐dimensional electroencephalography data ⋮ Representation and reconstruction of covariance operators in linear inverse problems ⋮ Dynamic filtering of static dipoles in magnetoencephalography ⋮ Local-aggregate modeling for big data via distributed optimization: Applications to neuroimaging ⋮ Quasi-periodic spatiotemporal models of brain activation in single-trial <scp>MEG</scp> experiments ⋮ Two-way regularization for MEG source reconstruction via multilevel coordinate descent

Uses Software

• BRENT
• PMA

Cites Work

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• Sparse principal component analysis via regularized low rank matrix approximation
• Functional principal components analysis via penalized rank one approximation
• Estimating evoked dipole responses in unknown spatially correlated noise with EEG/MEG arrays
• The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular Value Decompositions
• Biclustering via Sparse Singular Value Decomposition

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