plt.show()
# now the affected channel
affected_idx = raw.ch_names.index('MEG 1531')
plt.figure()
plt.plot(times, data[affected_idx], color='r')
plt.plot(times, data_clean[affected_idx], color='k')
plt.xlim(100, 106)
plt.show()
###############################################################################
# Validation: check ECG components extracted
# Export ICA as Raw object for subsequent processing steps in ICA space.
ica_raw = ica.sources_as_raw(raw, start=100., stop=160., picks=None)
from mne.preprocessing import find_ecg_events
# find ECG events
event_id = 999
events, _, _ = find_ecg_events(raw,
ch_name='MEG 1531',
event_id=event_id,
l_freq=8,
h_freq=16)
# pick components, create epochs and evoked in ICA space
ica_picks = np.arange(ica.n_components_)
ica_raw.info['bads'] = [] # selected components are exported as bad channels
pl.title('Affected channel MEG 1531 before cleaning.')
y0, y1 = pl.ylim()
# plot the component that correlates most with the ECG
pl.figure()
pl.plot(times, data_clean[affected_idx])
pl.title('Affected channel MEG 1531 after cleaning.')
pl.ylim(y0, y1)
pl.show()
###############################################################################
# Export ICA as raw for subsequent processing steps in ICA space.
from mne.layouts import make_grid_layout
ica_raw = ica.sources_as_raw(raw, start=start, stop=stop, picks=None)
print ica_raw.ch_names
ica_lout = make_grid_layout(ica_raw.info)
# Uncomment the following two lines to save sources and layout.
# ica_raw.save('ica_raw.fif')
# ica_lout.save(os.path.join(os.environ['HOME'], '.mne/lout/ica.lout'))
################################################################################
# To save an ICA session you can say:
# ica.save('my_ica.fif')
#
# You can later restore the session by saying:
# >>> from mne.preprocessing import read_ica
plt.title('Affected channel MEG 1531 before cleaning.')
y0, y1 = plt.ylim()
# plot the component that correlates most with the ECG
plt.figure()
plt.plot(times, data_clean[affected_idx], color='k')
plt.title('Affected channel MEG 1531 after cleaning.')
plt.ylim(y0, y1)
plt.show()
###############################################################################
# Export ICA as raw for subsequent processing steps in ICA space.
from mne.layouts import make_grid_layout
ica_raw = ica.sources_as_raw(raw, start=100., stop=160., picks=None)
print ica_raw.ch_names[:5] # just a few
ica_lout = make_grid_layout(ica_raw.info)
# Uncomment the following two lines to save sources and layout.
# ica_raw.save('ica_raw.fif')
# ica_lout.save(os.path.join(os.environ['HOME'], '.mne/lout/ica.lout'))
###############################################################################
# To save an ICA session you can say:
# ica.save('my_ica.fif')
#
# You can later restore the session by saying:
# >>> from mne.preprocessing import read_ica
pl.title('Affected channel MEG 1531 before cleaning.')
y0, y1 = pl.ylim()
# plot the component that correlates most with the ECG
pl.figure()
pl.plot(times, data_clean[affected_idx])
pl.title('Affected channel MEG 1531 after cleaning.')
pl.ylim(y0, y1)
pl.show()
###############################################################################
# Export ICA as raw for subsequent processing steps in ICA space.
from mne.layouts import make_grid_layout
ica_raw = ica.sources_as_raw(raw, start=start, stop=stop, picks=None)
print ica_raw.ch_names
ica_lout = make_grid_layout(ica_raw.info)
# Uncomment the following two lines to save sources and layout.
# ica_raw.save('ica_raw.fif')
# ica_lout.save(os.path.join(os.environ['HOME'], '.mne/lout/ica.lout'))
################################################################################
# To save an ICA session you can say:
# ica.save('my_ica.fif')
#
# You can later restore the session by saying:
# >>> from mne.preprocessing import read_ica
