def __init__(self, subject, settings=dict()):
self.subject = subject
self.settings = settings
if 'debug' in settings:
configure_custom(settings['debug'])
else:
configure_custom(debug=True)
if 'mne_log_level' in settings:
mne.set_log_level(settings['mne_log_level'])
else:
mne.set_log_level('INFO')
if 'sfreq' in settings:
self.downsample_sfreq = settings['sfreq']
else:
self.downsample_sfreq = 64
if 'layout' in settings:
self.layout = settings['layout']
else:
self.layout = mne.channels.read_layout('biosemi.lay')
if 'data_root' in settings:
self.data_root = settings['data_root']
else:
self.data_root = os.path.join(deepthought.DATA_PATH, 'OpenMIIR')
# load stimuli metadata version
self.stimuli_version = get_stimuli_version(subject)
# initial state
self.raw = None
self.ica = None
self.filtered = False
self.downsampled = False
def __init__(self, subject, settings=dict()):
self.subject = subject
self.settings = settings
if 'debug' in settings:
configure_custom(settings['debug'])
else:
configure_custom(debug=True)
if 'mne_log_level' in settings:
mne.set_log_level(settings['mne_log_level'])
else:
mne.set_log_level('INFO')
if 'sfreq' in settings:
self.downsample_sfreq = settings['sfreq']
else:
self.downsample_sfreq = 64
if 'layout' in settings:
self.layout = settings['layout']
else:
self.layout = mne.channels.read_layout('biosemi.lay')
if 'data_root' in settings:
self.data_root = settings['data_root']
else:
self.data_root = os.path.join(deepthought.DATA_PATH, 'OpenMIIR')
# load stimuli metadata version
self.stimuli_version = get_stimuli_version(subject)
# initial state
self.raw = None
self.ica = None
self.filtered = False
self.downsampled = False
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 process(self, target, metadata):
subject = metadata['subject']
stimulus_id = metadata['stimulus_id']
version = get_stimuli_version(subject)
bpm = self.meta[version][stimulus_id]['bpm']
return bpm
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
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 process(self, target, metadata):
subject = metadata['subject']
stimulus_id = metadata['stimulus_id']
version = get_stimuli_version(subject)
bpm = self.meta[version][stimulus_id]['bpm']
return bpm