Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging

Related tags

Deep LearningShICA
Overview

ShICA

CircleCI

Code accompanying the paper Shared Independent Component Analysis for Multi-subject Neuroimaging

Install

Move into the ShICA directory cd ShICA

Install ShICA pip install -e .

Reproduce synthetic experiments in Figure 2

Move into the experiments directory cd experiments

Run the bash script to produce results (should take approximately 3 minutes on a modern laptop) bash run_all.bash

Move into the plotting directory cd plotting

Run the bash script to produce figures from the results bash plot_all.bash

Figures are available in the figures directory.

Performances on Gaussian sources:

Full non Gaussian

Performances on non Gaussian sources:

Full Gaussian

Performances when some sources are Gaussian and some non-Gaussian:

Semi Gaussian

Note The current implementation uses only 10 seeds and 4 different number of samples in the curves so that computation time is low even on a laptop. In order to obtain exactly the same curves as in the paper you should modify the files rotation.py, full_nongaussian.py and semigaussian.py in the experiments directory so that

num_points = 20
seeds = np.arange(40)
ns = np.logspace(2, 5, num_points)

Documentation

https://hugorichard.github.io/ShICA/index.html

Owner
PhD Candidate Machine Learning
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