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Space–time event sparse penalization for magneto-/electroencephalography

Authors
Journal
NeuroImage
1053-8119
Publisher
Elsevier
Publication Date
Volume
46
Issue
4
Identifiers
DOI: 10.1016/j.neuroimage.2009.01.056
Keywords
  • M/Eeg Source Localization
  • Spatio-Temporal Priors
  • M/Eeg Inverse Problem
Disciplines
  • Computer Science
  • Mathematics
  • Medicine

Abstract

Abstract This article presents a new spatio-temporal method for M/EEG source reconstruction based on the assumption that only a small number of events, localized in space and/or time, are responsible for the measured signal. Each space–time event is represented using a basis function expansion which reflects the most relevant (or measurable) features of the signal. This model of neural activity leads naturally to a Bayesian likelihood function which balances the model fit to the data with the complexity of the model, where the complexity is related to the number of included events. A novel Expectation-Maximization algorithm which maximizes the likelihood function is presented. The new method is shown to be effective on several MEG simulations of neurological activity as well as data from a self-paced finger tapping experiment.

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