def check_trial_event_consistency(self):
meta = load_stimuli_metadata(self.data_root, self.stimuli_version)
sfreq = self.raw.info['sfreq']
n_errors = 0
for i, event in enumerate(self.trial_events[:-1]):
event_id = event[2]
start = event[0]
if event_id < 1000:
stim_id, cond = decode_event_id(event_id)
if cond in [1, 2]:
field = 'length_with_cue'
else:
field = 'length_without_cue'
sample_len = sfreq * meta[stim_id][field]
else:
sample_len = 1
next_start = self.trial_events[i + 1, 0]
if next_start < start + sample_len:
expected_len = sample_len / float(sfreq)
event_len = (next_start - start) / float(sfreq)
log.warn('warning: event {} starts before expected end of {}'.
format(self.trial_events[i + 1], event))
log.warn(
'expected length: {:.3f}s, real length: {:.3f}s, delta: {:.3f}s'
.format(expected_len, event_len, expected_len - event_len))
n_errors += 1
log.info('{} problems detected.'.format(n_errors))
def check_trial_event_consistency(self):
meta = load_stimuli_metadata(self.data_root, self.stimuli_version)
sfreq = self.raw.info['sfreq']
n_errors = 0
for i, event in enumerate(self.trial_events[:-1]):
event_id = event[2]
start = event[0]
if event_id < 1000:
stim_id, cond = decode_event_id(event_id)
if cond in [1,2]:
field = 'length_with_cue'
else:
field = 'length_without_cue'
sample_len = sfreq * meta[stim_id][field]
else:
sample_len = 1
next_start = self.trial_events[i+1, 0]
if next_start < start + sample_len:
expected_len = sample_len / float(sfreq)
event_len = (next_start - start) / float(sfreq)
log.warn('warning: event {} starts before expected end of {}'.format(self.trial_events[i+1], event))
log.warn('expected length: {:.3f}s, real length: {:.3f}s, delta: {:.3f}s'.format(
expected_len, event_len, expected_len-event_len))
n_errors += 1
log.info('{} problems detected.'.format(n_errors))
def __init__(self, data_root=None):
self.meta = dict()
for version in [1, 2]:
self.meta[version] = load_stimuli_metadata(data_root=data_root,
version=version)
def get_trial_epochs(raw,
trial_events,
stim_id,
condition,
subject=None,
stimuli_version=None,
meta=None,
include_cue=False,
picks=None,
debug=False):
assert subject is None or stimuli_version is None or meta is None
if meta is None:
if stimuli_version is None:
if subject is None:
raise RuntimeError(
'Either meta, stimuli_version or subject has to be specified.'
)
else:
stimuli_version = get_stimuli_version(subject)
meta = load_stimuli_metadata(version=stimuli_version)
events = filter_trial_events(trial_events, [stim_id], [condition])
if debug:
log.debug('selected events: {}'.format(len(events)))
log.debug(events)
start = 0
if condition in [1, 2]: # cued
if include_cue:
stop = meta[stim_id]['length_with_cue']
else:
# NOTE: start > 0 does not work; need to shift event time
offset = int(
np.floor(meta[stim_id]['length_of_cue'] * raw.info['sfreq']))
events[:, 0] += offset
stop = meta[stim_id]['length_without_cue']
else:
stop = meta[stim_id]['length_without_cue']
if debug:
log.debug('start: {} stop: {}'.format(start, stop))
log.debug(events)
if picks is None:
# default: all EEG channels including bad/interpolated
picks = mne.pick_types(raw.info,
meg=False,
eeg=True,
eog=False,
stim=False,
exclude=[])
epochs = mne.Epochs(raw,
events,
None,
tmin=start,
tmax=stop,
preload=True,
proj=False,
picks=picks,
verbose=False)
if debug:
log.debug(epochs)
return epochs
def __init__(self, data_root=None):
self.meta = dict()
for version in [1,2]:
self.meta[version] = load_stimuli_metadata(data_root=data_root, version=version)
