Open Access
September 2012 A two-way regularization method for MEG source reconstruction
Tian Siva Tian, Jianhua Z. Huang, Haipeng Shen, Zhimin Li
Ann. Appl. Stat. 6(3): 1021-1046 (September 2012). DOI: 10.1214/11-AOAS531


The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples.


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Tian Siva Tian. Jianhua Z. Huang. Haipeng Shen. Zhimin Li. "A two-way regularization method for MEG source reconstruction." Ann. Appl. Stat. 6 (3) 1021 - 1046, September 2012.


Published: September 2012
First available in Project Euclid: 31 August 2012

zbMATH: 1254.92059
MathSciNet: MR3012519
Digital Object Identifier: 10.1214/11-AOAS531

Keywords: inverse problem , MEG , spatio-temporal , two-way regularization

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 3 • September 2012
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