![]() |
FAµST (Flexible Approximate MUlti-layer Sparse Transforms)
3.38.14
|
Brain Source Localization demo. More...
Static Public Member Functions | |
def | sparse_coeffs (D, ntraining, sparsity) |
Generates sparse coefficients. More... | |
def | run (input_data_dir=get_data_dirpath(silent=True), output_dir=DEFT_RESULTS_DIR, on_gpu=False) |
This function performs brain source localization. More... | |
def | fig (input_dir=DEFT_RESULTS_DIR, output_dir=DEFT_FIG_DIR) |
Calls all fig*() functions of bsl demo. More... | |
def | fig_time_cmp (input_dir=DEFT_RESULTS_DIR, output_dir=DEFT_FIG_DIR, use_precomputed_data=False) |
Builds the time comparison figure for the BSL with the differents Faust representations of the MEG matrix. More... | |
def | fig_speedup (input_dir=DEFT_RESULTS_DIR, output_dir=DEFT_FIG_DIR) |
Builds the speedup comparison figure for the BSL with the differents Faust representations of the MEG matrix. More... | |
def | fig_convergence (input_dir=DEFT_RESULTS_DIR, output_dir=DEFT_FIG_DIR) |
This function builds a figure similar to the BSL figure (Fig 9) used in [1]. More... | |
Brain Source Localization demo.
|
static |
Calls all fig*() functions of bsl demo.
|
static |
This function builds a figure similar to the BSL figure (Fig 9) used in [1].
|
static |
Builds the speedup comparison figure for the BSL with the differents Faust representations of the MEG matrix.
|
static |
Builds the time comparison figure for the BSL with the differents Faust representations of the MEG matrix.
|
static |
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".
on_gpu | if True the demo is ran on GPU (if cuda backend is available). |
The MEG gain matrices used are the precomputed ones in
(in the installation directory of the FAuST toolbox)
[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.
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
|
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.