def _find_analogue_trigger_limit_sd(raw, events, anapick, tmin=-0.2, tmax=0.0):
epochs = Epochs(raw,
events,
tmin=tmin,
tmax=tmax,
picks=anapick,
baseline=(None, 0),
preload=True)
epochs._data = np.sqrt(epochs._data**2) # RECTIFY!
ave = epochs.average(picks=[0])
sde = epochs.standard_error(picks=[0])
return (ave.data.mean(),
5.0 * sde.data[0, np.where(sde.times < 0)].mean() *
np.sqrt(epochs.events.shape[0]))
def _get_epochs(subject):
# if already computed, lets load it from disk
epo_fname = paths('epochs_vhp', subject=subject)
if op.exists(epo_fname):
return load('epochs_vhp', subject=subject, preload=True)
# high pass filter and epoch
for block in range(1, 6):
raw = load('sss', subject=subject, block=block, preload=True)
# Explicit picking of channel to ensure same channels across subjects
picks = [
'STI101', 'EEG060', 'EOG061', 'EOG062', 'ECG063', 'EEG064',
'MISC004'
]
# Potentially add forgotten channels
ch_type = dict(STI='stim', EEG='eeg', EOG='eog', ECG='ecg', MIS='misc')
missing_chans = list()
for channel in picks:
if channel not in raw.ch_names:
missing_chans.append(channel)
if missing_chans:
info = create_info(missing_chans, raw.info['sfreq'],
[ch_type[ch[:3]] for ch in missing_chans])
raw.add_channels([
RawArray(np.zeros(
(len(missing_chans), raw.n_times)), info, raw.first_samp)
],
force_update_info=True)
# Select same channels order across subjects
picks = [np.where(np.array(raw.ch_names) == ch)[0][0] for ch in picks]
picks = np.r_[np.arange(306), picks]
# Filtered
raw.filter(2, 30, l_trans_bandwidth=.5, filter_length='30s', n_jobs=1)
# Ensure same sampling rate
if raw.info['sfreq'] != 1000.0:
raw.resample(1000.0)
# Select events
events = find_events(raw, stim_channel='STI101', shortest_event=1)
sel = np.where(events[:, 2] <= 255)[0]
events = events[sel, :]
# Compensate for delay (as measured manually with photodiod
events[1, :] += int(.050 * raw.info['sfreq'])
# Epoch continuous data
this_epochs = Epochs(raw,
events,
reject=None,
tmin=-.200,
tmax=1.6,
picks=picks,
baseline=None,
decim=10)
save(this_epochs, 'epo_block', subject=subject, block=block)
this_epochs._data = None
raw.data = None
del this_epochs, raw
epochs = list()
for block in range(1, 6):
this_epochs = load('epo_block', subject=subject, block=block)
epochs.append(this_epochs)
epochs = concatenate_epochs(epochs)
# save for faster retest
save(epochs, 'epochs_vhp', subject=subject, overwrite=True, upload=False)
return epochs
data= dataset.get_data()
df = pd.DataFrame()
l_freq = 0.; h_freq = 38.
#get Epochs
datasets = []
for subj_id, subj_data in data.items():
epochs_list = []
for sess_id, sess_data in subj_data.items():
for run_id, raw in sess_data.items():
events = find_events(raw)
onset,offset = dataset.interval
events[:,0] += onset*250
raw2 = raw.pick_types(eeg=True, meg=False, stim=False)
raw2 = raw2.filter(l_freq,h_freq)
epoch = Epochs(raw2, events, event_id=dataset.event_id, tmin=-0.5, tmax=4)
epoch._data = epoch.get_data()
#epoch.load_data()
epochs_list.append(epoch)
epochs = mne.concatenate_epochs(epochs_list)
#epoch to torch.Dataset
datasets.append(BcicDataset(epochs,subj_id))
with open('bcic_datasets_[0,38].pkl', 'wb', ) as f:
dill.dump(datasets, f)
def _get_epochs(subject):
# if already computed, lets load it from disk
epo_fname = paths('epochs_vhp', subject=subject)
if op.exists(epo_fname):
return load('epochs_vhp', subject=subject, preload=True)
# high pass filter and epoch
for block in range(1, 6):
raw = load('sss', subject=subject, block=block, preload=True)
# Explicit picking of channel to ensure same channels across subjects
picks = ['STI101', 'EEG060', 'EOG061', 'EOG062', 'ECG063', 'EEG064',
'MISC004']
# Potentially add forgotten channels
ch_type = dict(STI='stim', EEG='eeg', EOG='eog', ECG='ecg',
MIS='misc')
missing_chans = list()
for channel in picks:
if channel not in raw.ch_names:
missing_chans.append(channel)
if missing_chans:
info = create_info(missing_chans, raw.info['sfreq'],
[ch_type[ch[:3]] for ch in missing_chans])
raw.add_channels([RawArray(
np.zeros((len(missing_chans), raw.n_times)), info,
raw.first_samp)], force_update_info=True)
# Select same channels order across subjects
picks = [np.where(np.array(raw.ch_names) == ch)[0][0] for ch in picks]
picks = np.r_[np.arange(306), picks]
# Filtered
raw.filter(2, 30, l_trans_bandwidth=.5, filter_length='30s',
n_jobs=1)
# Ensure same sampling rate
if raw.info['sfreq'] != 1000.0:
raw.resample(1000.0)
# Select events
events = find_events(raw, stim_channel='STI101', shortest_event=1)
sel = np.where(events[:, 2] <= 255)[0]
events = events[sel, :]
# Compensate for delay (as measured manually with photodiod
events[1, :] += int(.050 * raw.info['sfreq'])
# Epoch continuous data
this_epochs = Epochs(raw, events, reject=None, tmin=-.200, tmax=1.6,
picks=picks, baseline=None, decim=10)
save(this_epochs, 'epo_block', subject=subject, block=block)
this_epochs._data = None
raw.data = None
del this_epochs, raw
epochs = list()
for block in range(1, 6):
this_epochs = load('epo_block', subject=subject, block=block)
epochs.append(this_epochs)
epochs = concatenate_epochs(epochs)
# save for faster retest
save(epochs, 'epochs_vhp', subject=subject, overwrite=True, upload=False)
return epochs
