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
<https://www.linkedin.com/in/igal-nazar/>*Igal Nazar*
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/>
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