Superresolution via sparsity constraints
SIAM Journal on Mathematical Analysis
Multiwavelets for Second-Kind Integral Equations
SIAM Journal on Numerical Analysis
Adaptive Wavelet Galerkin Methods for Linear Inverse Problems
SIAM Journal on Numerical Analysis
Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints
SIAM Journal on Numerical Analysis
Adaptive iterative thresholding algorithms for magnetoencephalography (MEG)
Journal of Computational and Applied Mathematics
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
SIAM Journal on Imaging Sciences
An iterative algorithm with joint sparsity constraints for magnetic tomography
MMCS'08 Proceedings of the 7th international conference on Mathematical Methods for Curves and Surfaces
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Hi-index | 0.00 |
EEG/MEG devices record external signals which are generated by the neuronal electric activity of the brain. The localization of the neuronal sources requires the solution of the neuroelectromagnetic inverse problem which is highly ill-posed and ill-conditioned. We provide an iterative thresholding algorithm for recovering neuroeletric current densities within the brain through combined EEG/MEG data. We use a joint sparsity constraint to promote solutions localized in small brain area, assuming that the vector components of the current densities possess the same sparse spatial pattern. At each iteration step, the EEG/MEG forward problem is numerically solved by a Galerkin boundary element method. Some numerical experiments on the localization of current dipole sources are also given. The numerical results show that joint sparsity constraints outperform classical regularization methods based on quadratic constraints.