SISSY: an efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity

Abstract : Over the past decades, a multitude of different brain source imaging algorithms have been developed to identify the neural generators underlying the surface electroencephalography measurements. While most of these techniques focus on determining the source positions, only a small number of recently developed algorithms provides an indication of the spatial extent of the distributed sources. In a recent comparison of brain source imaging approaches, the VB-SCCD algorithm has been shown to be one of the most promising algorithms among these methods. However, this technique suffers from several problems: it leads to amplitude-biased source estimates, it has difficulties in separating close sources, and it has a high computational complexity due to its implementation using second order cone programming. To overcome these problems, we propose to include an additional regularization term that imposes sparsity in the original source domain and to solve the resulting optimization problem using the alternating direction method of multipliers. Furthermore, we show that the algorithm yields more robust solutions by taking into account the temporal structure of the data. We also propose a new method to automatically threshold the estimated source distribution, which permits to delineate the active brain regions. The new algorithm, called Source Imaging based on Structured Sparsity (SISSY), is analyzed by means of realistic computer simulations and is validated on the clinical data of four patients.
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Article dans une revue
NeuroImage, Elsevier, 2017, 157, pp.157-172. 〈10.1016/j.neuroimage.2017.05.046〉
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Contributeur : Laurent Albera <>
Soumis le : lundi 16 octobre 2017 - 11:49:09
Dernière modification le : lundi 11 février 2019 - 14:12:05
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Hanna Becker, Laurent Albera, Pierre Comon, Jean-Claude Nunes, Rémi Gribonval, et al.. SISSY: an efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity. NeuroImage, Elsevier, 2017, 157, pp.157-172. 〈10.1016/j.neuroimage.2017.05.046〉. 〈inserm-01617155〉



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