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"""
===========================================
Compare the different ICA algorithms in MNE
===========================================
Different ICA algorithms are fit to raw MEG data, and the corresponding maps
are displayed.
"""
# Authors: Pierre Ablin <pierreablin@gmail.com>
#
# License: BSD (3-clause)
from time import time
import mne
from mne.preprocessing import ICA
from mne.datasets import sample
print(__doc__)
###############################################################################
# Read and preprocess the data. Preprocessing consists of:
#
# - MEG channel selection
# - 1-30 Hz band-pass filter
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
picks = mne.pick_types(raw.info)
reject = dict(mag=5e-12, grad=4000e-13)
raw.filter(1, 30, fir_design='firwin')
###############################################################################
# Define a function that runs ICA on the raw MEG data and plots the components
def run_ica(method):
ica = ICA(n_components=20, method=method, random_state=0)
t0 = time()
ica.fit(raw, picks=picks, reject=reject)
fit_time = time() - t0
title = ('ICA decomposition using %s (took %.1fs)' % (method, fit_time))
ica.plot_components(title=title)
###############################################################################
# FastICA
run_ica('fastica')
###############################################################################
# Picard
run_ica('picard')
###############################################################################
# Infomax
run_ica('infomax')
###############################################################################
# Extended Infomax
run_ica('extended-infomax')
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