Accelerated high-resolution EEG source imaging

Jing Qin, Tianyu Wu, Ying Li, Wotao Yin, Stanley Osher, Wentai Liu

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

3 Scopus citations


Electroencephalography (EEG) signal has been playing a crucial role in clinical diagnosis and treatment of neurological diseases. However, it is very challenging to efficiently reconstruct the high-resolution brain image from very few scalp EEG measurements due to high ill-posedness. Recently some efforts have been devoted to developing EEG source reconstruction methods using various forms of regularization, including the ℓ1-norm, the total variation (TV), as well as the fractional-order TV. However, since high-dimensional data are very large, these methods are difficult to implement. In this paper, we propose accelerated methods for EEG source imaging based on the TV regularization and its variants. Since the gradient/fractional-order gradient operators have coordinate friendly structures, we apply the Chambolle-Pock and ARock algorithms, along with diagonal preconditioning. In our algorithms, the coordinates of primal and dual variables are updated in an asynchronously parallel fashion. A variety of experiments show that the proposed algorithms have more rapid convergence than the state-of-the-art methods and have the potential to achieve the real-time temporal resolution.

Original languageEnglish
Title of host publication8th International IEEE EMBS Conference on Neural Engineering, NER 2017
Number of pages4
ISBN (Electronic)9781538619162
StatePublished - Aug 10 2017
Event8th International IEEE EMBS Conference on Neural Engineering, NER 2017 - Shanghai, China
Duration: May 25 2017May 28 2017

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554


Conference8th International IEEE EMBS Conference on Neural Engineering, NER 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering


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