# First, we create a helper function that iteratively applies the pearson
# correlation function to sources and returns an array of r values
# This is to illustrate the way ica.find_sources_raw works. Actually, this is
# the default score_func.
from scipy.stats import pearsonr
corr = lambda x, y: np.array([pearsonr(a, y.ravel()) for a in x])[:, 0]
# 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_raw(raw, target='MEG 1531', score_func=corr)
# get sources
sources = ica.get_sources_raw(raw, start=start_plot, stop=stop_plot)
# get times
times = raw.time_as_index(np.arange(stop_plot - start_plot))
# get maximum correlation index for ECG
ecg_source_idx = np.abs(ecg_scores).argmax()
pl.figure()
pl.plot(times, sources[ecg_source_idx])
pl.title('ICA source matching ECG')
pl.show()
# setup reasonable time window for inspection
start_plot, stop_plot = raw.time_as_index([100, 103])
# plot components
ica.plot_sources_raw(raw, start=start_plot, stop=stop_plot)
###############################################################################
# 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.
# We select the pearson correlation from scipy stats via string label.
# The function is internally modified to be applicable to 2D arrays and,
# hence, returns product-moment correlation scores for each ICA source.
eog_scores = ica.find_sources_raw(raw, target='EOG 061',
score_func='pearsonr')
# get sources for the entire time range.
sources = ica.get_sources_raw(raw)
# get maximum correlation index for ECG
eog_source_idx = np.abs(eog_scores).argmax()
###############################################################################
# Find ECG event onsets from ICA source
event_id = 999
eog_events = ica_find_eog_events(raw=raw, eog_source=sources[eog_source_idx],
event_id=event_id)
# Read epochs
###############################################################################
# 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.
# We select the pearson correlation from scipy stats via string label.
# The function is internally modified to be applicable to 2D arrays and,
# hence, returns product-moment correlation scores for each ICA source.
# pick ECG affected channel
ch_idx = raw.ch_names.index('MEG 1531')
ecg = raw[ch_idx, :][0]
ecg = mne.filter.high_pass_filter(ecg.ravel(), raw.info['sfreq'], 1.0)
ecg_scores = ica.find_sources_raw(raw, target=ecg, score_func='pearsonr')
# get sources for the entire time range.
sources = ica.get_sources_raw(raw)
# get maximum correlation index for ECG
ecg_source_idx = np.abs(ecg_scores).argmax()
# high pass filter source
ecg_source = mne.filter.high_pass_filter(sources[ecg_source_idx],
raw.info['sfreq'], 1.0)
###############################################################################
# Find ECG event onsets from ICA source
event_id = 999
###############################################################################
# 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.
# We select the pearson correlation from scipy stats via string label.
# The function is internally modified to be applicable to 2D arrays and,
# hence, returns product-moment correlation scores for each ICA source.
# pick ECG affected channel
ch_idx = raw.ch_names.index('MEG 1531')
ecg = raw[ch_idx, :][0]
ecg = mne.filter.high_pass_filter(ecg.ravel(), raw.info['sfreq'], 1.0)
ecg_scores = ica.find_sources_raw(raw, target=ecg, score_func='pearsonr')
# get sources for the entire time range.
sources = ica.get_sources_raw(raw)
# get maximum correlation index for ECG
ecg_source_idx = np.abs(ecg_scores).argmax()
# high pass filter source
ecg_source = mne.filter.high_pass_filter(sources[ecg_source_idx],
raw.info['sfreq'], 1.0)
###############################################################################
# Find ECG event onsets from ICA source
event_id = 999
