def test_ica_labels():
"""Test ICA labels."""
# The CTF data are uniquely well suited to testing the ICA.find_bads_
# methods
raw = read_raw_ctf(ctf_fname, preload=True)
# set the appropriate EEG channels to EOG and ECG
raw.set_channel_types({'EEG057': 'eog', 'EEG058': 'eog', 'EEG059': 'ecg'})
ica = ICA(n_components=4, random_state=0, max_iter=2, method='fastica',
allow_ref_meg=True)
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw)
_assert_ica_attributes(ica)
ica.find_bads_eog(raw, l_freq=None, h_freq=None)
picks = list(pick_types(raw.info, meg=False, eog=True))
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('eog', idx, raw.ch_names[ch]) in ica.labels_
assert 'eog' in ica.labels_
for key in ('ecg', 'ref_meg', 'ecg/ECG-MAG'):
assert key not in ica.labels_
ica.find_bads_ecg(raw, l_freq=None, h_freq=None, method='correlation',
threshold='auto')
picks = list(pick_types(raw.info, meg=False, ecg=True))
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('ecg', idx, raw.ch_names[ch]) in ica.labels_
for key in ('ecg', 'eog'):
assert key in ica.labels_
for key in ('ref_meg', 'ecg/ECG-MAG'):
assert key not in ica.labels_
# derive reference ICA components and append them to raw
ica_rf = ICA(n_components=2, random_state=0, max_iter=2,
allow_ref_meg=True)
with pytest.warns(UserWarning, match='did not converge'):
ica_rf.fit(raw.copy().pick_types(meg=False, ref_meg=True))
icacomps = ica_rf.get_sources(raw)
# rename components so they are auto-detected by find_bads_ref
icacomps.rename_channels({c: 'REF_' + c for c in icacomps.ch_names})
# and add them to raw
raw.add_channels([icacomps])
ica.find_bads_ref(raw, l_freq=None, h_freq=None, method="separate")
picks = pick_channels_regexp(raw.ch_names, 'REF_ICA*')
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('ref_meg', idx,
raw.ch_names[ch]) in ica.labels_
ica.find_bads_ref(raw, l_freq=None, h_freq=None, method="together")
assert 'ref_meg' in ica.labels_
for key in ('ecg', 'eog', 'ref_meg'):
assert key in ica.labels_
assert 'ecg/ECG-MAG' not in ica.labels_
ica.find_bads_ecg(raw, l_freq=None, h_freq=None, threshold='auto')
for key in ('ecg', 'eog', 'ref_meg', 'ecg/ECG-MAG'):
assert key in ica.labels_
def test_ica_labels():
"""Test ICA labels."""
# The CTF data are uniquely well suited to testing the ICA.find_bads_
# methods
raw = read_raw_ctf(ctf_fname, preload=True)
# derive reference ICA components and append them to raw
icarf = ICA(n_components=2, random_state=0, max_iter=2, allow_ref_meg=True)
with pytest.warns(UserWarning, match='did not converge'):
icarf.fit(raw.copy().pick_types(meg=False, ref_meg=True))
icacomps = icarf.get_sources(raw)
# rename components so they are auto-detected by find_bads_ref
icacomps.rename_channels({c: 'REF_' + c for c in icacomps.ch_names})
# and add them to raw
raw.add_channels([icacomps])
# set the appropriate EEG channels to EOG and ECG
raw.set_channel_types({'EEG057': 'eog', 'EEG058': 'eog', 'EEG059': 'ecg'})
ica = ICA(n_components=4, random_state=0, max_iter=2, method='fastica')
with pytest.warns(UserWarning, match='did not converge'):
ica.fit(raw)
ica.find_bads_eog(raw, l_freq=None, h_freq=None)
picks = list(pick_types(raw.info, meg=False, eog=True))
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('eog', idx, raw.ch_names[ch]) in ica.labels_
assert 'eog' in ica.labels_
for key in ('ecg', 'ref_meg', 'ecg/ECG-MAG'):
assert key not in ica.labels_
ica.find_bads_ecg(raw, l_freq=None, h_freq=None, method='correlation')
picks = list(pick_types(raw.info, meg=False, ecg=True))
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('ecg', idx, raw.ch_names[ch]) in ica.labels_
for key in ('ecg', 'eog'):
assert key in ica.labels_
for key in ('ref_meg', 'ecg/ECG-MAG'):
assert key not in ica.labels_
ica.find_bads_ref(raw, l_freq=None, h_freq=None)
picks = pick_channels_regexp(raw.ch_names, 'REF_ICA*')
for idx, ch in enumerate(picks):
assert '{}/{}/{}'.format('ref_meg', idx,
raw.ch_names[ch]) in ica.labels_
for key in ('ecg', 'eog', 'ref_meg'):
assert key in ica.labels_
assert 'ecg/ECG-MAG' not in ica.labels_
ica.find_bads_ecg(raw, l_freq=None, h_freq=None)
for key in ('ecg', 'eog', 'ref_meg', 'ecg/ECG-MAG'):
assert key in ica.labels_
# %%
# Run the "together" algorithm.
raw_tog = raw.copy()
ica_kwargs = dict(
method='picard',
fit_params=dict(tol=1e-4), # use a high tol here for speed
)
all_picks = mne.pick_types(raw_tog.info, meg=True, ref_meg=True)
ica_tog = ICA(n_components=60,
max_iter='auto',
allow_ref_meg=True,
**ica_kwargs)
ica_tog.fit(raw_tog, picks=all_picks)
# low threshold (2.0) here because of cropped data, entire recording can use
# a higher threshold (2.5)
bad_comps, scores = ica_tog.find_bads_ref(raw_tog, threshold=2.0)
# Plot scores with bad components marked.
ica_tog.plot_scores(scores, bad_comps)
# Examine the properties of removed components. It's clear from the time
# courses and topographies that these components represent external,
# intermittent noise.
ica_tog.plot_properties(raw_tog, picks=bad_comps)
# Remove the components.
raw_tog = ica_tog.apply(raw_tog, exclude=bad_comps)
# %%
# Cleaned data:
raw_tog.plot_psd(fmax=30)
