Sparse Spatio-temporal Inference of Electromagnetic Brain Sources

Carsten Stahlhut, Hagai Thomas Attias, David Wipf, Lars Kai Hansen, Srikantan S. Nagarajan

    Research output: Contribution to journalConference articleResearchpeer-review


    The electromagnetic brain activity measured via MEG (or EEG) can be interpreted as arising from a collection of current dipoles or sources located throughout the cortex. Because the number of candidate locations for these sources is much larger than the number of sensors, source reconstruction involves solving an inverse problem that is severely underdetermined. Bayesian graphical models provide a powerful means of incorporating prior assumptions that narrow the solution space and lead to tractable posterior distributions over the unknown sources given the observed data. In particular, this paper develops a hierarchical, spatio-temporal Bayesian model that accommodates the principled computation of sparse spatial and smooth temporal M/EEG source reconstructions consistent with neurophysiological assumptions in a variety of event-related imaging paradigms. The underlying methodology relies on the notion of automatic relevance determination (ARD) to express the unknown sources via a small collection of spatio-temporal basis functions. Experiments with several data sets provide evidence that the proposed model leads to improved source estimates. The underlying methodology is also well-suited for estimation problems that arise from other brain imaging modalities such as functional or diffusion weighted MRI.
    Original languageEnglish
    Book seriesLecture Notes in Computer Science
    Pages (from-to)157-164
    Publication statusPublished - 2010
    EventInternational Workshop on Machine Learning in Medical Imaging (MLMI) : In conjunction with MICCAI 2010 -
    Duration: 1 Jan 2010 → …


    ConferenceInternational Workshop on Machine Learning in Medical Imaging (MLMI) : In conjunction with MICCAI 2010
    Period01/01/2010 → …


    • underdetermined inverse problems, M/EEG source reconstruction, probabilistic graphical models, variational Bayes, ARD

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