Abstract
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. © Institute of Mathematical Statistics, 2012.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1021-1046 |
| Number of pages | 26 |
| Journal | The Annals of Applied Statistics |
| Volume | 6 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 2012 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): KUS-CI-016-04
Acknowledgements: Supported in part by the University of Houston New Faculty Research Program.Supported in part by NCI (CA57030), NSF (DMS-09-07170, DMS-10-07618) and King AbdullahUniversity of Science and Technology (KUS-CI-016-04).Supported in part by NIDA (1 RC1 DA029425-01) and NSF (CMMI-0800575, DMS-11-06912).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.