# As we don't have an ECG channel we use one that correlates a lot with heart
# beats: 'MEG 1531'. We can directly pass the name to the find_sources method.
# In our example, the find_sources method returns and array of correlation
# scores for each ICA source.
ecg_ch_name = 'MEG 1531'
ecg_scores = ica.find_sources_epochs(epochs,
target=ecg_ch_name,
score_func='pearsonr')
# get the source most correlated with the ECG.
ecg_source_idx = np.argsort(np.abs(ecg_scores))[-1]
# get sources as epochs object and inspect some trial
some_trial = 10
title = 'Source most similar to ECG'
ica.plot_sources_epochs(epochs[some_trial], ecg_source_idx, title=title)
# As we have an EOG channel, we can use it to detect the source.
eog_scores = ica.find_sources_epochs(epochs,
target='EOG 061',
score_func='pearsonr')
# get maximum correlation index for EOG
eog_source_idx = np.abs(eog_scores).argmax()
# As the subject did not constantly move her eyes, the movement artifacts
# may remain hidden when plotting single epochs.
# Plotting the identified source across epochs reveals
# considerable EOG artifacts.
title = 'Source most similar to EOG'
ica.plot_sources_epochs(epochs, eog_source_idx, title=title)
tmin, tmax, event_id = -0.2, 0.5, 1
# baseline = None
baseline = (None, 0)
reject = None
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=baseline, preload=True, reject=reject)
# fit sources from epochs or from raw (both works for epochs)
ica.decompose_epochs(epochs)
# plot components for one epoch of interest
# A distinct cardiac component should be visible
ica.plot_sources_epochs(epochs, epoch_idx=13, n_components=25)
###############################################################################
# Automatically find the ECG component using correlation with ECG signal
# As we don't have an ECG channel we use one that correlates a lot with heart
# beats: 'MEG 1531'. We can directly pass the name to the find_sources method.
# In our example, the find_sources method returns and array of correlation
# scores for each ICA source.
ecg_scores = ica.find_sources_epochs(epochs, target='MEG 1531',
score_func='pearsonr')
# get maximum correlation index for ECG
ecg_source_idx = np.abs(ecg_scores).argmax()
# As we don't have an ECG channel we use one that correlates a lot with heart
# beats: 'MEG 1531'. We can directly pass the name to the find_sources method.
# In our example, the find_sources method returns and array of correlation
# scores for each ICA source.
ecg_ch_name = 'MEG 1531'
ecg_scores = ica.find_sources_epochs(epochs, target=ecg_ch_name,
score_func='pearsonr')
# get the source most correlated with the ECG.
ecg_source_idx = np.argsort(np.abs(ecg_scores))[-1]
# get sources as epochs object and inspect some trial
some_trial = 10
title = 'Source most similar to ECG'
ica.plot_sources_epochs(epochs[some_trial], ecg_source_idx, title=title)
# As we have an EOG channel, we can use it to detect the source.
eog_scores = ica.find_sources_epochs(epochs, target='EOG 061',
score_func='pearsonr')
# get maximum correlation index for EOG
eog_source_idx = np.abs(eog_scores).argmax()
# As the subject did not constantly move her eyes, the movement artifacts
# may remain hidden when plotting single epochs.
# Plotting the identified source across epochs reveals
# considerable EOG artifacts.
title = 'Source most similar to EOG'
ica.plot_sources_epochs(epochs, eog_source_idx, title=title)
# baseline = None
baseline = (None, 0)
reject = None
events = mne.find_events(raw, stim_channel="STI 014")
epochs = mne.Epochs(
raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline=baseline, preload=True, reject=reject
)
# fit sources from epochs or from raw (both works for epochs)
ica.decompose_epochs(epochs)
# plot components for one epoch of interest
# A distinct cardiac component should be visible
ica.plot_sources_epochs(epochs, epoch_idx=13, n_components=25)
###############################################################################
# Automatically find the ECG component using correlation with ECG signal
# As we don't have an ECG channel we use one that correlates a lot with heart
# beats: 'MEG 1531'. We can directly pass the name to the find_sources method.
# In our example, the find_sources method returns and array of correlation
# scores for each ICA source.
ecg_scores = ica.find_sources_epochs(epochs, target="MEG 1531", score_func="pearsonr")
# get maximum correlation index for ECG
ecg_source_idx = np.abs(ecg_scores).argmax()
print "#%i -- ICA component resembling the ECG" % ecg_source_idx
