Accelerated High-Resolution EEG Source Imaging

Jing Qin1, Tianyu Wu2, Ying Li2, Wotao Yin2, Stanley Osher3, Wentai Liu2

  • 1Montana State University
  • 2University of California, Los Angeles
  • 3Univesity of California, Los Angeles



Contributed Papers


11:30 - 13:30 | Fri 26 May | Emerald III, Rose, Narcissus & Jasmine | FrPS1T1

Poster I

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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 brain image given sources from very few scalp 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 $ell_1$-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.

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