File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: EEG/MEG source reconstruction with spatial-temporal two-way regularized regression

TitleEEG/MEG source reconstruction with spatial-temporal two-way regularized regression
Authors
KeywordsMEG
Sparsity
Roughness penalization
Inverse problem
Graph Laplacian
Coordinate descent
Issue Date2013
Citation
Neuroinformatics, 2013, v. 11, n. 4, p. 477-493 How to Cite?
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.
Persistent Identifierhttp://hdl.handle.net/10722/219721
ISSN
2015 Impact Factor: 2.864
2015 SCImago Journal Rankings: 1.304

 

DC FieldValueLanguage
dc.contributor.authorTian, Tian Siva-
dc.contributor.authorHuang, Jianhua Z.-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorLi, Zhimin-
dc.date.accessioned2015-09-23T02:57:48Z-
dc.date.available2015-09-23T02:57:48Z-
dc.date.issued2013-
dc.identifier.citationNeuroinformatics, 2013, v. 11, n. 4, p. 477-493-
dc.identifier.issn1539-2791-
dc.identifier.urihttp://hdl.handle.net/10722/219721-
dc.description.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.-
dc.languageeng-
dc.relation.ispartofNeuroinformatics-
dc.subjectMEG-
dc.subjectSparsity-
dc.subjectRoughness penalization-
dc.subjectInverse problem-
dc.subjectGraph Laplacian-
dc.subjectCoordinate descent-
dc.titleEEG/MEG source reconstruction with spatial-temporal two-way regularized regression-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s12021-013-9193-2-
dc.identifier.pmid23842791-
dc.identifier.scopuseid_2-s2.0-84886301636-
dc.identifier.volume11-
dc.identifier.issue4-
dc.identifier.spage477-
dc.identifier.epage493-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats