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%matplotlib inline
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# Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.preprocessing import ICA
from mne.preprocessing import create_ecg_epochs, create_eog_epochs
from mne.datasets import sample
Setup paths and prepare raw data.
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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)
raw.filter(1, None, fir_design='firwin') # already lowpassed @ 40
raw.set_annotations(mne.Annotations([1], [10], 'BAD'))
raw.plot(block=True)
# For the sake of example we annotate first 10 seconds of the recording as
# 'BAD'. This part of data is excluded from the ICA decomposition by default.
# To turn this behavior off, pass ``reject_by_annotation=False`` to
# :meth:`mne.preprocessing.ICA.fit`.
raw.set_annotations(mne.Annotations([0], [10], 'BAD'))
1) Fit ICA model using the FastICA algorithm.
Other available choices are picard or infomax.
The default method in MNE is FastICA, which along with Infomax is one of the most widely used ICA algorithm. Picard is a new algorithm that is expected to converge faster than FastICA and Infomax, especially when the aim is to recover accurate maps with a low tolerance parameter, see [1]_ for more information.
We pass a float value between 0 and 1 to select n_components based on the percentage of variance explained by the PCA components.
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ica = ICA(n_components=0.95, method='fastica', random_state=0, max_iter=100)
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
# low iterations -> does not fully converge
ica.fit(raw, picks=picks, decim=3, reject=dict(mag=4e-12, grad=4000e-13))
# maximum number of components to reject
n_max_ecg, n_max_eog = 3, 1 # here we don't expect horizontal EOG components
2) identify bad components by analyzing latent sources.
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title = 'Sources related to %s artifacts (red)'
# generate ECG epochs use detection via phase statistics
ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5, picks=picks)
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs, method='ctps')
ica.plot_scores(scores, exclude=ecg_inds, title=title % 'ecg', labels='ecg')
show_picks = np.abs(scores).argsort()[::-1][:5]
ica.plot_sources(raw, show_picks, exclude=ecg_inds, title=title % 'ecg')
ica.plot_components(ecg_inds, title=title % 'ecg', colorbar=True)
ecg_inds = ecg_inds[:n_max_ecg]
ica.exclude += ecg_inds
# detect EOG by correlation
eog_inds, scores = ica.find_bads_eog(raw)
ica.plot_scores(scores, exclude=eog_inds, title=title % 'eog', labels='eog')
show_picks = np.abs(scores).argsort()[::-1][:5]
ica.plot_sources(raw, show_picks, exclude=eog_inds, title=title % 'eog')
ica.plot_components(eog_inds, title=title % 'eog', colorbar=True)
eog_inds = eog_inds[:n_max_eog]
ica.exclude += eog_inds
3) Assess component selection and unmixing quality.
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# estimate average artifact
ecg_evoked = ecg_epochs.average()
ica.plot_sources(ecg_evoked, exclude=ecg_inds) # plot ECG sources + selection
ica.plot_overlay(ecg_evoked, exclude=ecg_inds) # plot ECG cleaning
eog_evoked = create_eog_epochs(raw, tmin=-.5, tmax=.5, picks=picks).average()
ica.plot_sources(eog_evoked, exclude=eog_inds) # plot EOG sources + selection
ica.plot_overlay(eog_evoked, exclude=eog_inds) # plot EOG cleaning
# check the amplitudes do not change
ica.plot_overlay(raw) # EOG artifacts remain
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# To save an ICA solution you can say:
# ica.save('my_ica.fif')
# You can later load the solution by saying:
# from mne.preprocessing import read_ica
# read_ica('my_ica.fif')
# Apply the solution to Raw, Epochs or Evoked like this:
# ica.apply(epochs)