def plot_performance_artifact_rejection(meg_raw,
ica,
fnout_fig,
meg_clean=None,
show=False,
proj=False,
verbose=False):
'''
Creates a performance image of the data before
and after the cleaning process.
'''
from mne.preprocessing import find_ecg_events, find_eog_events
from jumeg import jumeg_math as jmath
name_ecg = 'ECG 001'
name_eog_hor = 'EOG 001'
name_eog_ver = 'EOG 002'
event_id_ecg = 999
event_id_eog = 998
tmin_ecg = -0.4
tmax_ecg = 0.4
tmin_eog = -0.4
tmax_eog = 0.4
picks = mne.pick_types(meg_raw.info,
meg=True,
ref_meg=False,
exclude='bads')
# as we defined x% of the explained variance as noise (e.g. 5%)
# we will remove this noise from the data
if meg_clean:
meg_clean_given = True
else:
meg_clean_given = False
meg_clean = ica.apply(meg_raw,
exclude=ica.exclude,
n_pca_components=ica.n_components_,
copy=True)
# plotting parameter
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# check if ECG and EOG was recorded in addition
# to the MEG data
ch_names = meg_raw.info['ch_names']
# ECG
if name_ecg in ch_names:
nstart = 0
nrange = 1
else:
nstart = 1
nrange = 1
# EOG
if name_eog_ver in ch_names:
nrange = 2
y_figsize = 6 * nrange
perf_art_rej = np.zeros(2)
# ToDo: How can we avoid popping up the window if show=False ?
pl.ioff()
pl.figure('performance image', figsize=(12, y_figsize))
pl.clf()
# ECG, EOG: loop over all artifact events
for i in range(nstart, nrange):
# get event indices
if i == 0:
baseline = (None, None)
event_id = event_id_ecg
idx_event, _, _ = find_ecg_events(meg_raw,
event_id,
ch_name=name_ecg,
verbose=verbose)
idx_ref_chan = meg_raw.ch_names.index(name_ecg)
tmin = tmin_ecg
tmax = tmax_ecg
pl1 = nrange * 100 + 21
pl2 = nrange * 100 + 22
text1 = "CA: original data"
text2 = "CA: cleaned data"
elif i == 1:
baseline = (None, None)
event_id = event_id_eog
idx_event = find_eog_events(meg_raw,
event_id,
ch_name=name_eog_ver,
verbose=verbose)
idx_ref_chan = meg_raw.ch_names.index(name_eog_ver)
tmin = tmin_eog
tmax = tmax_eog
pl1 = nrange * 100 + 21 + (nrange - nstart - 1) * 2
pl2 = nrange * 100 + 22 + (nrange - nstart - 1) * 2
text1 = "OA: original data"
text2 = "OA: cleaned data"
# average the signals
raw_epochs = mne.Epochs(meg_raw,
idx_event,
event_id,
tmin,
tmax,
picks=picks,
baseline=baseline,
proj=proj,
verbose=verbose)
cleaned_epochs = mne.Epochs(meg_clean,
idx_event,
event_id,
tmin,
tmax,
picks=picks,
baseline=baseline,
proj=proj,
verbose=verbose)
ref_epochs = mne.Epochs(meg_raw,
idx_event,
event_id,
tmin,
tmax,
picks=[idx_ref_chan],
baseline=baseline,
proj=proj,
verbose=verbose)
raw_epochs_avg = raw_epochs.average()
cleaned_epochs_avg = cleaned_epochs.average()
ref_epochs_avg = np.average(ref_epochs.get_data(),
axis=0).flatten() * -1.0
times = raw_epochs_avg.times * 1e3
if np.max(raw_epochs_avg.data) < 1:
factor = 1e15
else:
factor = 1
ymin = np.min(raw_epochs_avg.data) * factor
ymax = np.max(raw_epochs_avg.data) * factor
# plotting data before cleaning
pl.subplot(pl1)
pl.plot(times, raw_epochs_avg.data.T * factor, 'k')
pl.title(text1)
# plotting reference signal
pl.plot(times, jmath.rescale(ref_epochs_avg, ymin, ymax), 'r')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
# print some info
textstr1 = 'num_events=%d\nEpochs: tmin, tmax = %0.1f, %0.1f' \
% (len(idx_event), tmin, tmax)
pl.text(times[10],
1.09 * ymax,
textstr1,
fontsize=10,
verticalalignment='top',
bbox=props)
# plotting data after cleaning
pl.subplot(pl2)
pl.plot(times, cleaned_epochs_avg.data.T * factor, 'k')
pl.title(text2)
# plotting reference signal again
pl.plot(times, jmath.rescale(ref_epochs_avg, ymin, ymax), 'r')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
# print some info
perf_art_rej[i] = calc_performance(raw_epochs_avg, cleaned_epochs_avg)
# ToDo: would be nice to add info about ica.excluded
if meg_clean_given:
textstr1 = 'Performance: %d\nFrequency Correlation: %d'\
% (perf_art_rej[i],
calc_frequency_correlation(raw_epochs_avg, cleaned_epochs_avg))
else:
textstr1 = 'Performance: %d\nFrequency Correlation: %d\n# ICs: %d\nExplained Var.: %d'\
% (perf_art_rej[i],
calc_frequency_correlation(raw_epochs_avg, cleaned_epochs_avg),
ica.n_components_, ica.n_components * 100)
pl.text(times[10],
1.09 * ymax,
textstr1,
fontsize=10,
verticalalignment='top',
bbox=props)
if show:
pl.show()
# save image
pl.savefig(fnout_fig + '.png', format='png')
pl.close('performance image')
pl.ion()
return perf_art_rej
def plot_performance_artifact_rejection(meg_raw, ica, fnout_fig,
meg_clean=None, show=False,
proj=False, verbose=False,
name_ecg='ECG 001', name_eog='EOG 002'):
'''
Creates a performance image of the data before
and after the cleaning process.
'''
from mne.preprocessing import find_ecg_events, find_eog_events
from jumeg import jumeg_math as jmath
# name_ecg = 'ECG 001'
# name_eog_hor = 'EOG 001'
# name_eog_ver = 'EOG 002'
event_id_ecg = 999
event_id_eog = 998
tmin_ecg = -0.4
tmax_ecg = 0.4
tmin_eog = -0.4
tmax_eog = 0.4
picks = mne.pick_types(meg_raw.info, meg=True, ref_meg=False,
exclude='bads')
# as we defined x% of the explained variance as noise (e.g. 5%)
# we will remove this noise from the data
if meg_clean:
meg_clean_given = True
else:
meg_clean_given = False
meg_clean = ica.apply(meg_raw.copy(), exclude=ica.exclude,
n_pca_components=ica.n_components_)
# plotting parameter
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# check if ECG and EOG was recorded in addition
# to the MEG data
ch_names = meg_raw.info['ch_names']
# ECG
if name_ecg in ch_names:
nstart = 0
nrange = 1
else:
nstart = 1
nrange = 1
# EOG
if name_eog in ch_names:
nrange = 2
y_figsize = 6 * nrange
perf_art_rej = np.zeros(2)
# ToDo: How can we avoid popping up the window if show=False ?
pl.ioff()
pl.figure('performance image', figsize=(12, y_figsize))
pl.clf()
# ECG, EOG: loop over all artifact events
for i in range(nstart, nrange):
# get event indices
if i == 0:
baseline = (None, None)
event_id = event_id_ecg
idx_event, _, _ = find_ecg_events(meg_raw, event_id,
ch_name=name_ecg,
verbose=verbose)
idx_ref_chan = meg_raw.ch_names.index(name_ecg)
tmin = tmin_ecg
tmax = tmax_ecg
pl1 = nrange * 100 + 21
pl2 = nrange * 100 + 22
text1 = "CA: original data"
text2 = "CA: cleaned data"
elif i == 1:
baseline = (None, None)
event_id = event_id_eog
idx_event = find_eog_events(meg_raw, event_id, ch_name=name_eog,
verbose=verbose)
idx_ref_chan = meg_raw.ch_names.index(name_eog)
tmin = tmin_eog
tmax = tmax_eog
pl1 = nrange * 100 + 21 + (nrange - nstart - 1) * 2
pl2 = nrange * 100 + 22 + (nrange - nstart - 1) * 2
text1 = "OA: original data"
text2 = "OA: cleaned data"
# average the signals
raw_epochs = mne.Epochs(meg_raw, idx_event, event_id, tmin, tmax,
picks=picks, baseline=baseline, proj=proj,
verbose=verbose)
cleaned_epochs = mne.Epochs(meg_clean, idx_event, event_id, tmin, tmax,
picks=picks, baseline=baseline, proj=proj,
verbose=verbose)
ref_epochs = mne.Epochs(meg_raw, idx_event, event_id, tmin, tmax,
picks=[idx_ref_chan], baseline=baseline,
proj=proj, verbose=verbose)
raw_epochs_avg = raw_epochs.average()
cleaned_epochs_avg = cleaned_epochs.average()
ref_epochs_avg = np.average(ref_epochs.get_data(), axis=0).flatten() * -1.0
times = raw_epochs_avg.times * 1e3
if np.max(raw_epochs_avg.data) < 1:
factor = 1e15
else:
factor = 1
ymin = np.min(raw_epochs_avg.data) * factor
ymax = np.max(raw_epochs_avg.data) * factor
# plotting data before cleaning
pl.subplot(pl1)
pl.plot(times, raw_epochs_avg.data.T * factor, 'k')
pl.title(text1)
# plotting reference signal
pl.plot(times, jmath.rescale(ref_epochs_avg, ymin, ymax), 'r')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
# print some info
textstr1 = 'num_events=%d\nEpochs: tmin, tmax = %0.1f, %0.1f' \
% (len(idx_event), tmin, tmax)
pl.text(times[10], 1.09 * ymax, textstr1, fontsize=10,
verticalalignment='top', bbox=props)
# plotting data after cleaning
pl.subplot(pl2)
pl.plot(times, cleaned_epochs_avg.data.T * factor, 'k')
pl.title(text2)
# plotting reference signal again
pl.plot(times, jmath.rescale(ref_epochs_avg, ymin, ymax), 'r')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
# print some info
perf_art_rej[i] = calc_performance(raw_epochs_avg, cleaned_epochs_avg)
# ToDo: would be nice to add info about ica.excluded
if meg_clean_given:
textstr1 = 'Performance: %d\nFrequency Correlation: %d'\
% (perf_art_rej[i],
calc_frequency_correlation(raw_epochs_avg, cleaned_epochs_avg))
else:
textstr1 = 'Performance: %d\nFrequency Correlation: %d\n# ICs: %d\nExplained Var.: %d'\
% (perf_art_rej[i],
calc_frequency_correlation(raw_epochs_avg, cleaned_epochs_avg),
ica.n_components_, ica.n_components * 100)
pl.text(times[10], 1.09 * ymax, textstr1, fontsize=10,
verticalalignment='top', bbox=props)
if show:
pl.show()
# save image
pl.savefig(fnout_fig + '.png', format='png')
pl.close('performance image')
pl.ion()
return perf_art_rej
def plot_compare_brain_responses(fname_orig,
fname_new,
event_id=1,
tmin=-0.2,
tmax=0.5,
stim_name=None,
proj=False,
show=False):
'''
Function showing performance of signal with brain responses from
selected components only. Plots the evoked (avg) signal of original
data and brain responses only data along with difference between them.
fname_orig, fname_new: str
stim_ch: str (default STI 014)
show: bool (default False)
'''
pl.ioff()
if show:
pl.ion()
# Get the stimulus channel for special event from the fname_new
# make a judgment, whether this raw data include more than one kind of event.
# if True, use the first event as the start point of the epoches.
# Adjust the size of the time window based on different connditions
basename = fname_new.split('-raw.fif')[0]
# if stim_name is given we assume that the input data are raw and
# cleaned data ('cleaned' means data were cardiac and ocular artifacts
# were rejected)
if stim_name:
fnout_fig = basename + '-' + stim_name + '.png'
else:
stim_name = fname_new.rsplit(',ctpsbr')[0].rsplit('ar,')[1]
# Construct file names.
fnout_fig = basename + '.png'
if ',' in stim_name:
stim_ch = 'STI 014'
elif stim_name == 'trigger':
stim_ch = 'STI 014'
elif stim_name == 'response':
stim_ch = 'STI 013'
# Read raw, calculate events, epochs, and evoked.
raw_orig = mne.io.Raw(fname_orig, preload=True)
raw_br = mne.io.Raw(fname_new, preload=True)
events = mne.find_events(raw_orig, stim_channel=stim_ch, consecutive=True)
events = mne.find_events(raw_br, stim_channel=stim_ch, consecutive=True)
picks_orig = mne.pick_types(raw_orig.info, meg=True, exclude='bads')
picks_br = mne.pick_types(raw_br.info, meg=True, exclude='bads')
epochs_orig = mne.Epochs(raw_orig,
events,
event_id,
proj=proj,
tmin=tmin,
tmax=tmax,
picks=picks_orig,
preload=True)
epochs_br = mne.Epochs(raw_br,
events,
event_id,
proj=proj,
tmin=tmin,
tmax=tmax,
picks=picks_br,
preload=True)
evoked_orig = epochs_orig.average()
evoked_br = epochs_br.average()
times = evoked_orig.times * 1e3
if np.max(evoked_orig.data) < 1:
factor = 1e15
else:
factor = 1
ymin = np.min(evoked_orig.data) * factor
ymax = np.max(evoked_orig.data) * factor
# Make the comparison plot.
pl.figure('Compare raw data', figsize=(14, 5))
pl.subplot(1, 2, 1)
pl.plot(times, evoked_orig.data.T * factor, 'k', linewidth=0.5)
pl.plot(times, evoked_br.data.T * factor, 'r', linewidth=0.5)
pl.title('Signal before (black) and after (red) cleaning')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
# print out some information
textstr1 = 'Performance: %d\nFrequency Correlation: %d'\
% (calc_performance(evoked_orig, evoked_br),
calc_frequency_correlation(evoked_orig, evoked_br))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
pl.text(times[10],
1.09 * ymax,
textstr1,
fontsize=10,
verticalalignment='top',
bbox=props)
pl.subplot(1, 2, 2)
evoked_diff = evoked_orig - evoked_br
pl.plot(times, evoked_diff.data.T * factor, 'k', linewidth=0.5)
pl.title('Difference signal')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
pl.savefig(fnout_fig, format='png')
pl.close('Compare raw data')
pl.ion()
def plot_compare_brain_responses(fname_orig, fname_new, event_id=1,
tmin=-0.2, tmax=0.5, stim_name=None,
proj=False, show=False):
'''
Function showing performance of signal with brain responses from
selected components only. Plots the evoked (avg) signal of original
data and brain responses only data along with difference between them.
fname_orig, fname_new: str
stim_ch: str (default STI 014)
show: bool (default False)
'''
pl.ioff()
if show:
pl.ion()
# Get the stimulus channel for special event from the fname_new
# make a judgment, whether this raw data include more than one kind of event.
# if True, use the first event as the start point of the epoches.
# Adjust the size of the time window based on different connditions
basename = fname_new.split('-raw.fif')[0]
# if stim_name is given we assume that the input data are raw and
# cleaned data ('cleaned' means data were cardiac and ocular artifacts
# were rejected)
if stim_name:
fnout_fig = basename + '-' + stim_name + '.png'
else:
stim_name = fname_new.rsplit(',ctpsbr')[0].rsplit('ar,')[1]
# Construct file names.
fnout_fig = basename + '.png'
if ',' in stim_name:
stim_ch = 'STI 014'
elif stim_name == 'trigger':
stim_ch = 'STI 014'
elif stim_name == 'response':
stim_ch = 'STI 013'
# Read raw, calculate events, epochs, and evoked.
raw_orig = mne.io.Raw(fname_orig, preload=True)
raw_br = mne.io.Raw(fname_new, preload=True)
events = mne.find_events(raw_orig, stim_channel=stim_ch, consecutive=True)
events = mne.find_events(raw_br, stim_channel=stim_ch, consecutive=True)
picks_orig = mne.pick_types(raw_orig.info, meg=True, exclude='bads')
picks_br = mne.pick_types(raw_br.info, meg=True, exclude='bads')
epochs_orig = mne.Epochs(raw_orig, events, event_id, proj=proj,
tmin=tmin, tmax=tmax, picks=picks_orig,
preload=True)
epochs_br = mne.Epochs(raw_br, events, event_id, proj=proj,
tmin=tmin, tmax=tmax, picks=picks_br, preload=True)
evoked_orig = epochs_orig.average()
evoked_br = epochs_br.average()
times = evoked_orig.times * 1e3
if np.max(evoked_orig.data) < 1:
factor = 1e15
else:
factor = 1
ymin = np.min(evoked_orig.data) * factor
ymax = np.max(evoked_orig.data) * factor
# Make the comparison plot.
pl.figure('Compare raw data', figsize=(14, 5))
pl.subplot(1, 2, 1)
pl.plot(times, evoked_orig.data.T * factor, 'k', linewidth=0.5)
pl.plot(times, evoked_br.data.T * factor, 'r', linewidth=0.5)
pl.title('Signal before (black) and after (red) cleaning')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
# print out some information
textstr1 = 'Performance: %d\nFrequency Correlation: %d'\
% (calc_performance(evoked_orig, evoked_br),
calc_frequency_correlation(evoked_orig, evoked_br))
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
pl.text(times[10], 1.09 * ymax, textstr1, fontsize=10,
verticalalignment='top', bbox=props)
pl.subplot(1, 2, 2)
evoked_diff = evoked_orig - evoked_br
pl.plot(times, evoked_diff.data.T * factor, 'k', linewidth=0.5)
pl.title('Difference signal')
pl.xlim(times[0], times[len(times) - 1])
pl.ylim(1.1 * ymin, 1.1 * ymax)
pl.savefig(fnout_fig, format='png')
pl.close('Compare raw data')
pl.ion()
