reject=reject)
random_state = np.random.RandomState(42)
###############################################################################
# Setup ICA seed decompose data, then access and plot sources.
# for more background information visit the plot_ica_from_raw.py example
# 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=random_state)
ica.decompose_epochs(epochs, decim=2)
print(ica)
# 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.
noise_cov=None, random_state=0)
print ica
# get epochs
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')
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False, picks=picks,
baseline=baseline, preload=True, reject=reject)
random_state = np.random.RandomState(42)
###############################################################################
# Setup ICA seed decompose data, then access and plot sources.
# for more background information visit the plot_ica_from_raw.py example
# 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=random_state)
ica.decompose_epochs(epochs, decim=2)
print(ica)
# 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
print ica
# get epochs
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")
