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dc.contributor.authorLim, Michael
dc.contributor.authorAles, Justin M.
dc.contributor.authorCottereau, Benoit R.
dc.contributor.authorHastie, Trevor
dc.contributor.authorNorcia, Anthony M.
dc.identifier.citationLim , M , Ales , J M , Cottereau , B R , Hastie , T & Norcia , A M 2017 , ' Sparse EEG/MEG source estimation via a group lasso ' , PLoS One , vol. 12 , no. 6 , e0176835 .
dc.identifier.otherPURE: 250269138
dc.identifier.otherPURE UUID: 0a2882f8-01c4-4bff-9fb1-9115c254d13f
dc.identifier.otherRIS: urn:FBB6CB4855D58F83E6BAD3690FFD8B6E
dc.identifier.otherScopus: 85020692916
dc.identifier.otherWOS: 000403088400004
dc.descriptionThis work was supported by EY018875, National Institutes of Health; EY015790, National Institutes of Health; DMS-1007719, National Science Foundation; and RO1-EB001988-15, National Institutes of Health.en
dc.description.abstractNon-invasive recordings of human brain activity through electroencephalography (EEG) or magnetoencelphalography (MEG) are of value for both basic science and clinical applications in sensory, cognitive, and affective neuroscience. Here we introduce a new approach to estimating the intra-cranial sources of EEG/MEG activity measured from extra-cranial sensors. The approach is based on the group lasso, a sparse-prior inverse that has been adapted to take advantage of functionally-defined regions of interest for the definition of physiologically meaningful groups within a functionally-based common space. Detailed simulations using realistic source-geometries and data from a human Visual Evoked Potential experiment demonstrate that the group-lasso method has improved performance over traditional ℓ2 minimum-norm methods. In addition, we show that pooling source estimates across subjects over functionally defined regions of interest results in improvements in the accuracy of source estimates for both the group-lasso and minimum-norm approaches.
dc.relation.ispartofPLoS Oneen
dc.rights© 2017 Lim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectBF Psychologyen
dc.subjectQA Mathematicsen
dc.titleSparse EEG/MEG source estimation via a group lassoen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Psychology and Neuroscienceen
dc.description.statusPeer revieweden

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