def generate_beat_events(self, verbose=None):
assert self.filtered is True
assert self.downsampled is False
raw = self.raw
## generate beat events and epochs before downsampling
# read trial events
if hasattr(self, 'trial_events'):
trial_events = self.trial_events
else:
trial_events = mne.find_events(raw, stim_channel='STI 014', shortest_event=0)
# generate simple beat events with same ID (10000)
beat_events = generate_beat_events(trial_events,
version=self.stimuli_version,
beat_event_id_generator=simple_beat_event_id_generator,
verbose=verbose)
# FIXME: read from settings
picks = mne.pick_types(raw.info, meg=False, eeg=True, eog=True, stim=True, exclude=[])
event_id = None # any
tmin = -0.2 # start of each epoch (200ms before the trigger)
tmax = 0.8 # end of each epoch (600ms after the trigger) - longest beat is 0.57s long
detrend = 0 # remove dc
# reject = dict(eog=250e-6) # TODO: optionally reject epochs
beat_epochs = mne.Epochs(raw, beat_events, event_id,
tmin, tmax, preload=True,
proj=False, picks=picks, verbose=False)
print beat_epochs
self.beat_epochs = beat_epochs
def generate_beat_events(self, verbose=None):
assert self.filtered is True
assert self.downsampled is False
raw = self.raw
## generate beat events and epochs before downsampling
# read trial events
if hasattr(self, 'trial_events'):
trial_events = self.trial_events
else:
trial_events = mne.find_events(raw, stim_channel='STI 014', shortest_event=0)
# generate simple beat events with same ID (10000)
beat_events = generate_beat_events(trial_events,
version=self.stimuli_version,
beat_event_id_generator=simple_beat_event_id_generator,
verbose=verbose)
# FIXME: read from settings
picks = mne.pick_types(raw.info, meg=False, eeg=True, eog=True, stim=True, exclude=[])
event_id = None # any
tmin = -0.2 # start of each epoch (200ms before the trigger)
tmax = 0.8 # end of each epoch (600ms after the trigger) - longest beat is 0.57s long
detrend = 0 # remove dc
# reject = dict(eog=250e-6) # TODO: optionally reject epochs
beat_epochs = mne.Epochs(raw, beat_events, event_id,
tmin, tmax, preload=True,
proj=False, picks=picks, verbose=False)
print beat_epochs
self.beat_epochs = beat_epochs
def load_and_preprocess_raw(subject,
# default preprocessing params for BCMI 2015 experiments
onsets='audio',
interpolate_bad_channels=True,
reference_mastoids=True,
l_freq=0.5,
h_freq=30,
sfreq=64,
ica_cleaning=True,
verbose=None,
):
raw, trial_events = preprocessing.load_and_preprocess_raw(
subject,
onsets=onsets,
interpolate_bad_channels=interpolate_bad_channels,
reference_mastoids=reference_mastoids,
l_freq=l_freq, h_freq=h_freq,
sfreq=sfreq,
ica_cleaning=ica_cleaning,
verbose=verbose)
stimuli_version = get_stimuli_version(subject)
# generate beat events - we need them to find the downbeat times
beat_events = generate_beat_events(trial_events,
include_cue_beats=False, # IMPORTANT!!!
# beat_event_id_generator=simple_beat_event_id_generator, # -> event_id=10000
exclude_stimulus_ids=[],
exclude_condition_ids=[],
verbose=verbose,
version=stimuli_version)
if verbose:
log.debug('beat events: {}'.format(beat_events.shape))
return raw, trial_events, beat_events
def get_subject_beat_epochs(
subject,
raw,
events=None,
tmin=-0.2,
tmax=0.3,
stimulus_ids='any', # 1..4, 11..14, 21..24
conditions='any', # 1..4
beat_counts='any', # 1..4
cue_value='any', # 0=cue, 1=regular
picks=None,
verbose=False):
version = get_stimuli_version(subject)
sfreq = raw.info['sfreq']
if events is None:
trial_events = mne.find_events(raw,
stim_channel='STI 014',
shortest_event=0,
verbose=verbose)
else:
trial_events = events
beat_events = generate_beat_events(
trial_events,
include_cue_beats=True,
sr=sfreq,
version=version,
)
# print beat_events.shape
# optional filtering
beat_events = filter_beat_events(beat_events,
stimulus_ids=stimulus_ids,
conditions=conditions,
beat_counts=beat_counts,
cue_value=cue_value)
# print beat_events.shape
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=[])
# extract epochs from raw data
epochs = mne.Epochs(raw,
beat_events,
event_id=None,
tmin=tmin,
tmax=tmax,
preload=True,
proj=False,
picks=picks,
verbose=None)
return epochs
