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# -*- coding: utf-8 -*-
"""
.. _ex-ica-comp:
===========================================
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()
meg_path = data_path / 'MEG' / 'sample'
raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname).crop(0, 60).pick('meg').load_data()
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, fit_params=None):
ica = ICA(n_components=20, method=method, fit_params=fit_params,
max_iter='auto', random_state=0)
t0 = time()
ica.fit(raw, 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('infomax', fit_params=dict(extended=True))
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