A Hierarchical Bayesian M/EEG Imaging Method Correcting for Incomplete Spatio-Temporal Priors

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

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

Abstract

In this paper we present a hierarchical Bayesian model, to tackle the highly ill-posed problem that follows with MEG and EEG source imaging. Our model promotes spatiotemporal patterns through the use of both spatial and temporal basis functions. While in contrast to most previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatiotemporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio-temporal prior belief. Both artificial data and real EEG data is used to demonstrate the efficacy of the model.
Original languageEnglish
Title of host publication2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)
PublisherIEEE
Publication date2013
Pages560-563
ISBN (Print)978-1-4673-6456-0
DOIs
Publication statusPublished - 2013
Event10th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - San Francisco, United States
Duration: 7 Apr 201311 Apr 2013
http://www.biomedicalimaging.org/2013/
http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=20048

Conference

Conference10th IEEE International Symposium on Biomedical Imaging
CountryUnited States
CitySan Francisco
Period07/04/201311/04/2013
Internet address
SeriesInternational Symposium on Biomedical Imaging. Proceedings
ISSN1945-7928

Keywords

  • EEG
  • MEG
  • Inverse problem
  • Spatio-temporal prior
  • Variational Bayes

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