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Brain source imaging: from sparse to tensor models

Abstract : A number of application areas such as biomedical engineering require solving an underdetermined linear inverse problem. In such a case, it is necessary to make assumptions on the sources to restore identifiability. This problem is encountered in brain source imaging when identifying the source signals from noisy electroencephalographic or magnetoencephalographic measurements. This inverse problem has been widely studied during the last decades, giving rise to an impressive number of methods using different priors. Nevertheless, a thorough study of the latter, including especially sparse and tensor-based approaches, is still missing. In this paper, we propose i) a taxonomy of the algorithms based on methodological considerations, ii) a discussion of identifiability and convergence properties, advantages, drawbacks, and application domains of various techniques, and iii) an illustration of the performance of selected methods on identical data sets. Directions for future research in the area of biomedical imaging are eventually provided.
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Contributor : Laurent Albera <>
Submitted on : Tuesday, September 1, 2015 - 2:20:18 PM
Last modification on : Thursday, January 14, 2021 - 11:24:58 AM
Long-term archiving on: : Wednesday, April 26, 2017 - 6:56:00 PM


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Hanna Becker, Laurent Albera, Pierre Comon, Rémi Gribonval, Fabrice Wendling, et al.. Brain source imaging: from sparse to tensor models. IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2015, 32 (6), pp.100-112. ⟨10.1109/MSP.2015.2413711⟩. ⟨hal-01190559⟩



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