[Mne_analysis] Finding the alternative to Matlab's runica inmne

Igal Nazar igal at brainster-tech.com
Thu Nov 9 15:30:49 EST 2017
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```Hi all,

I'm dealing with big issues trying to find an alternative to code running
on Matlab using runica.m. Right now the code calls runica with the
following parameters:
'lrate', 0.001
'extended', 1
'random_setting', 'default '

Moreover, no pca is performed.

The variables that interest me in the output of the function are: weights,
sphere.

In python - trying to follow these instruction (
https://martinos.org/mne/stable/auto_tutorials/plot_artifacts_correction_ica.html
just until the fitting step), that's what I did:

n_components = 14  # if float, select n_components by explained variance of PCA
method = 'extended-infomax'  # for comparison with EEGLAB try
"extended-infomax" here
decim = 1  # we need sufficient statistics, not all time points -> saves time

# we will also set state of the random number generator - ICA is a
# non-deterministic algorithm, but we want to have the same decomposition
# and the same order of components each time this tutorial is run
random_state = 0

# create an ICA instance called ica
ica = mne.preprocessing.ICA(n_components=n_components, method=method,
random_state=random_state, max_iter=512, max_pca_components=None)
picks_eeg = mne.pick_types(raw.info, meg=False, eeg=True, eog=False,)
ica.fit(raw, picks=picks_eeg, decim=decim)

my questions are:

1. is it possible to run ICA without PCA in mne?
2. what is the equevialents of Matlab's variables: weights and sphere?

Hope I'm clear enough,
Thanks,

Igal

R&D Engineer

igal at brainster-tech.com
+ 972 52 6701713
<https://www.brainster-tech.com/>
<https://www.brainster-tech.com/> <https://blog.brainster-tech.com/>