Structured Sparsity Regularization Approach to the EEG Inverse Problem

Jair Montoya-Martinez, Antonio Artes-Rodriguez, Lars Kai Hansen, Massimiliano Pontil

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    Localization of brain activity involves solving the EEG inverse problem, which is an undetermined ill-posed problem. We propose a novel approach consisting in estimating, using structured sparsity regularization techniques, the Brain Electrical Sources (BES) matrix directly in the spatio-temporal source space. We use proximal splitting optimization methods, which are efficient optimization techniques, with good convergence rates and with the ability to handle large nonsmooth convex problems, which is the typical scenario in the EEG inverse problem. We have evaluated our approach under a simulated scenario, consisting in estimating a synthetic BES matrix with 5124 sources. We report results using ℓ1 (LASSO), ℓ1/ℓ2 (Group LASSO) and ℓ1 + ℓ1/ℓ2 (Sparse Group LASSO) regularizers.
    Original languageEnglish
    Title of host publication2012 3rd International Workshop on Cognitive Information Processing (CIP)
    Number of pages6
    PublisherIEEE
    Publication date2012
    ISBN (Print)978-1-4673-1877-8
    DOIs
    Publication statusPublished - 2012
    Event3rd International Workshop on Cognitive Information Processing (CIP) - Baiona, Spain
    Duration: 28 May 201230 May 2012
    http://cip2012.tsc.uc3m.es/

    Workshop

    Workshop3rd International Workshop on Cognitive Information Processing (CIP)
    CountrySpain
    CityBaiona
    Period28/05/201230/05/2012
    Internet address

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