EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression
KAUST Grant NumberKUS-CI-016-04
Online Publication Date2013-07-11
Print Publication Date2013-10
Permanent link to this recordhttp://hdl.handle.net/10754/598047
MetadataShow full item record
AbstractIn this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously through minimizing a single penalized least squares criterion. The novelty of our methodology is the simultaneous consideration of three desirable properties of the reconstructed source signals, that is, spatial focality, spatial smoothness, and temporal smoothness. The desirable properties are achieved by using three separate penalty functions in the penalized regression framework. Specifically, we impose a roughness penalty in the temporal domain for temporal smoothness, and a sparsity-inducing penalty and a graph Laplacian penalty in the spatial domain for spatial focality and smoothness. We develop a computational efficient multilevel block coordinate descent algorithm to implement the method. Using a simulation study with several settings of different spatial complexity and two real MEG examples, we show that the proposed method outperforms existing methods that use only a subset of the three penalty functions. © 2013 Springer Science+Business Media New York.
CitationTian TS, Huang JZ, Shen H, Li Z (2013) EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression. Neuroinformatics 11: 477–493. Available: http://dx.doi.org/10.1007/s12021-013-9193-2.
SponsorsThis work is supported in part by NIDA (1 RC1 DA029425-01), NSF (DMS-09-07170, DMS-10-07618, CMMI-0800575, DMS-11-06912, DMS-12-08952, and DMS-12-08786), and King Abdullah University of Science and Technology (KUS-CI-016-04).
CollectionsPublications Acknowledging KAUST Support
- A Subspace Pursuit-based Iterative Greedy Hierarchical solution to the neuromagnetic inverse problem.
- Authors: Babadi B, Obregon-Henao G, Lamus C, Hämäläinen MS, Brown EN, Purdon PL
- Issue date: 2014 Feb 15
- Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals.
- Authors: Engemann DA, Gramfort A
- Issue date: 2015 Mar
- Spatio-temporal regularization in linear distributed source reconstruction from EEG/MEG: a critical evaluation.
- Authors: Dannhauer M, Lämmel E, Wolters CH, Knösche TR
- Issue date: 2013 Apr
- Simultaneous EEG and MEG source reconstruction in sparse electromagnetic source imaging.
- Authors: Ding L, Yuan H
- Issue date: 2013 Apr
- A spatially-regularized dynamic source localization algorithm for EEG.
- Authors: Pirondini E, Babadi B, Lamus C, Brown EN, Purdon PL
- Issue date: 2012