Probabilistic M/EEG source imaging from sparse spatio-temporal event structure

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

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    Abstract

    While MEG and EEG source imaging methods have to tackle a severely ill-posed problem their success can be stated as their ability to constrain the solutions using appropriate priors. In this paper we propose a hierarchical Bayesian model facilitating spatio-temporal patterns through the use of both spatial and temporal basis functions. We demonstrate the efficacy of the model on both artificial data and real EEG data.
    Original languageEnglish
    Publication date2012
    Number of pages7
    Publication statusPublished - 2012
    Event2nd NIPS Workshop on Machine Learning and Interpretation in NeuroImaging (MLINI 2012) - Lake Tahoe, Nevada, United States
    Duration: 7 Dec 20128 Dec 2012
    https://sites.google.com/site/nipsmlini2012/

    Conference

    Conference2nd NIPS Workshop on Machine Learning and Interpretation in NeuroImaging (MLINI 2012)
    CountryUnited States
    CityLake Tahoe, Nevada
    Period07/12/201208/12/2012
    Internet address

    Keywords

    • M/EEG
    • Spatio-temporal patterns
    • Variational Bayes

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