MEG/EEG source localization using spatio-temporal sparse representations

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Abstract

Inverse MEG/EEG problem is known to be ill-posed and no single solution can be found without utilizing some prior knowledge about the nature of signal sources, the way the signals are propagating and finally collected by the sensors. The signals are assumed to have a sparse representation in appropriate domain, e.g. wavelet transform, and spatial locality of sources is assumed, the fact that MEG/EEG data comes from physiological source justifies such assumption. Spatial information is utilized through MEG/EEG forward model, which is used when looking for an inverse solution. Finally, we formulate an optimization problem that incorporates both the sparsity and the locality assumptions, and physical considerations about the model. The optimization problem is solved using an augmented Lagrangian framework with truncated Newton method for the inner iteration. © Springer-Verlag 2004.

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Polonsky, A., & Zibulevsky, M. (2004). MEG/EEG source localization using spatio-temporal sparse representations. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 1001–1008. https://doi.org/10.1007/978-3-540-30110-3_126

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