pyfaust.demo.bsl Class Reference

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...

Detailed Description

Brain Source Localization demo.

Member Function Documentation

fig()

def pyfaust.demo.bsl.fig (   input_dir = DEFT_RESULTS_DIR,   output_dir = DEFT_FIG_DIR  )

static

Calls all fig*() functions of bsl demo.

Note

Must be call after the bsl.run function.

fig_convergence()

def pyfaust.demo.bsl.fig_convergence (   input_dir = DEFT_RESULTS_DIR,   output_dir = DEFT_FIG_DIR  )

static

This function builds a figure similar to the BSL figure (Fig 9) used in [1].

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

fig_speedup()

def pyfaust.demo.bsl.fig_speedup (   input_dir = DEFT_RESULTS_DIR,   output_dir = DEFT_FIG_DIR  )

static

Builds the speedup comparison figure for the BSL with the differents Faust representations of the MEG matrix.

fig_time_cmp()

def pyfaust.demo.bsl.fig_time_cmp (   input_dir = DEFT_RESULTS_DIR,   output_dir = DEFT_FIG_DIR,   use_precomputed_data = False  )

static

Builds the time comparison figure for the BSL with the differents Faust representations of the MEG matrix.

run()

def pyfaust.demo.bsl.run (   input_data_dir = get_data_dirpath(silent=True),   output_dir = DEFT_RESULTS_DIR,   on_gpu = False  )

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".

Parameters

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

get_data_dirpath()+"/faust_MEG_rcg_X.mat"

(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

sparse_coeffs()

def pyfaust.demo.bsl.sparse_coeffs (   D,   ntraining,   sparsity  )

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

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The documentation for this class was generated from the following file:

• demo.py