EEG source imaging with spatio-temporal tomographic nonnegative independent component analysis

Pedro A. Valdés-Sosa, Mayrim Vega-Hernández, José Miguel Sánchez-Bornot, Eduardo Martínez-Montes, María Antonieta Bobes

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    45 Citations (Scopus)

    Abstract

    This article describes a spatio-temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of "atoms" or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth-in effect integrating ESI with NN-ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio-temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital-fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model.

    Original languageEnglish
    Pages (from-to)1898-1910
    Number of pages13
    JournalHuman Brain Mapping
    Volume30
    Issue number6
    DOIs
    Publication statusPublished - 1 Jun 2009

    Keywords

    • EEG
    • ICA
    • Inverse solution
    • MEG
    • Orthogonal
    • Semi-negative matrix factorization
    • Spatio-temporal source imaging

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