EEG source imaging based on spatial and temporal graph structures

Jing Qin, Feng Liu, Shouyi Wang, Jay Rosenberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

EEG serves as an essential tool for brain source localization due to its high temporal resolution. However, the inference of brain activities from the EEG data is, in general, a challenging ill-posed inverse problem. To better retrieve task related discriminative source patches from strong spontaneous background signals, we propose a novel EEG source imaging model based on spatial and temporal graph structures. In particular, graph fractional-order total variation (gFOTV) is used to enhance spatial smoothness, and the label information of brain state is enclosed in a temporal graph regularization term to guarantee intra-class consistency of estimated sources. The proposed model is efficiently solved by the alternating direction method of multipliers (ADMM). A two-stage algorithm is proposed as well to further improve the result. Numerical experiments have shown that our method localizes source extents more effectively than the benchmark methods.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Pages1-6
Number of pages6
ISBN (Electronic)9781538618417
DOIs
StatePublished - Mar 8 2018
Event7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017 - Montreal, Canada
Duration: Nov 28 2017Dec 1 2017

Publication series

NameProceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Volume2018-January

Conference

Conference7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Country/TerritoryCanada
CityMontreal
Period11/28/1712/1/17

Keywords

  • Alternating Direction Method of Multiplier (ADMM)
  • EEG Source Imaging
  • Graph Fractional-Order Total Variation
  • Graph Regularization

ASJC Scopus subject areas

  • Signal Processing
  • Radiology Nuclear Medicine and imaging

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