Brain Source Localization demo.
This function performs brain source localization.
It uses several gain matrices [2], including FAuSTs, and OMP solver. It reproduces the source localization experiment of [1]. The results are stored in output_dir+"results_BSL_user.mat".
- Parameters
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on_gpu | if True the demo is ran on GPU (if cuda backend is available). |
- DURATION
- Computations should take around 3 minutes.
The MEG gain matrices used are the precomputed ones in
(in the installation directory of the FAuST toolbox)
- References
- [1] Le Magoarou L. and Gribonval R., "Flexible multi-layer
sparse approximations of matrices and applications", Journal of Selected Topics in Signal Processing, 2016. https://hal.archives-ouvertes.fr/hal-01167948v1
- [2] A. Gramfort, M. Luessi, E. Larson, D. Engemann, D.
Strohmeier, C. Brodbeck, L. Parkkonen, M. Hamalainen, MNE
software for processing MEG and EEG data http://www.ncbi.nlm.nih.gov/pubmed/24161808, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119
def pyfaust.demo.bsl.sparse_coeffs |
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D, |
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ntraining, |
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sparsity |
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static |
Generates sparse coefficients.
Gamma = sparse_coeffs(D, ntraining, sparsity) generates ntraining sparse vectors stacked in a matrix Gamma.
Each sparse vector is of size the number of atoms in the dictionary D, its support is drawn uniformly at random and each non-zero entry is iid Gaussian.
- References
- [1] Le Magoarou L. and Gribonval R., "Learning computationally efficient
dictionaries and their implementation as fast transforms", submitted to NIPS 2014