max_n_components=100,
noise_cov=None,
random_state=0)
# For maximum rejection performance we will compute the decomposition on
# the entire time range
# decompose sources for raw data, select n_components by explained variance
ica.decompose_raw(raw, start=None, stop=None, picks=picks)
print ica
# 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.
# 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)
# 1 minute exposure should be sufficient for artifact detection. # However, rejection performance may significantly improve when using # the entire data range start, stop = raw.time_as_index([100, 160]) # decompose sources for raw data ica.decompose_raw(raw, start=start, stop=stop, picks=picks) print ica sources = ica.get_sources_raw(raw, start=start, stop=stop) # 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. # 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.
random_state=0)
# For maximum rejection performance we will compute the decomposition on
# the entire time range
# decompose sources for raw data, select n_components by explained variance
ica.decompose_raw(raw, start=None, stop=None, picks=picks)
print ica
sources = ica.get_sources_raw(raw)
# 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)
