# plot spatial sensitivities of a few ICA components
title = 'Spatial patterns of ICA components (Magnetometers)'
source_idx = range(35, 50)
ica.plot_topomap(source_idx, ch_type='mag')
plt.suptitle(title, fontsize=12)
###############################################################################
# Automatically find ECG and EOG component using correlation coefficient.
# 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')
# plot spatial sensitivities of a few ICA components
title = 'Spatial patterns of ICA components (Magnetometers)'
source_idx = range(35, 50)
ica.plot_topomap(source_idx, ch_type='mag')
plt.suptitle(title, fontsize=12)
###############################################################################
# Automatically find ECG and EOG component using correlation coefficient.
# 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()
# 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 ############################################################################### # Automatically find the EOG component using correlation with EOG signal # 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()
# 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
###############################################################################
# Automatically find the EOG component using correlation with EOG signal
# 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
# fit sources from epochs or from raw (both works for epochs)
ica = ICA(n_components=0.90, n_pca_components=64, max_pca_components=100,
noise_cov=None, random_state=0)
ica.decompose_epochs(epochs)
print ica
###############################################################################
# Automatically find ECG and EOG component using correlation coefficient.
# 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()
# get sources from concatenated epochs
sources = ica.get_sources_epochs(epochs, concatenate=True)
# plot first epoch
times = epochs.times
first_trial = np.arange(len(times))
pl.figure()
pl.title('Source most correlated with the ECG channel')
pl.plot(times, sources[ecg_source_idx, first_trial].T, color='r')
pl.xlabel('Time (s)')
