def load(subject, event_type):
# Behavior
fname = op.join(path_data, subject, 'behavior_%s.hdf5' % event_type)
events = read_hdf5(fname)
# add explicit conditions
events = complete_behavior(events)
# MEG
fname = op.join(path_data, subject, 'epochs_%s.fif' % event_type)
epochs = mne.read_epochs(fname)
return epochs, events
def load(subject, event_type):
# Behavior
fname = op.join(path_data, subject, 'behavior_%s.hdf5' % event_type)
events = read_hdf5(fname)
# add explicit conditions
events = complete_behavior(events)
# MEG
if target_baseline:
fname = op.join(path_data, subject,
'epochs_tf_%s.fif' % event_type) # noqa
else:
fname = op.join(path_data, subject,
'epochs_tf_%s_bsl.fif' % event_type) # noqa
epochs = mne.read_epochs(fname)
# epochs.decimate(10)
return epochs, events
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['xtick.labelsize'] = 16
plt.rcParams['ytick.labelsize'] = 16
all_correct = list()
cueangle_correct = list()
cuesfreq_correct = list()
cueleft_correct = list()
cueright_correct = list()
for subject in subjects:
# Read behav file (hdf5)
print '**********' + subject + '************'
fname = op.join(path_data, subject, 'behavior_target.hdf5')
events = read_hdf5(fname)
events = complete_behavior(events)
# Select behav perf on all trials
isfixed = np.where(events['is_eye_fixed'] == 1)
iscorrect = np.array(events['is_correct'])
iscorrect_fixed = iscorrect[isfixed]
if len(iscorrect_fixed) != 800:
warnings.warn("Total isfixed trial is not 800")
print 'total is:' + str(len(iscorrect_fixed))
perc = sum(iscorrect_fixed) / len(iscorrect_fixed)
all_correct.append(perc)
# behav perf only cue angle
cue_angle = np.where(events['cue_type'] == 'angle')
if len(cue_angle[0]) != 400:
warnings.warn("Total trial with cue angle is not 400")
print 'total is:' + str(len(cue_angle[0]))
def make_cue_epoch_tf(subject):
"""Create cue epochs during WM task """
fname_raw = op.join(path_data, subject)
# Read behavioral file
fname_bhv = list()
files = os.listdir(op.join(path_data, subject, 'behavdata'))
fname_bhv.extend(([
op.join(fname_raw + '/behavdata/') + f for f in files if 'WorkMem' in f
]))
for fname_behavior in fname_bhv:
events_behavior = get_events_from_mat(fname_behavior)
# Read raw MEG data and extract event triggers
runs = list()
files = os.listdir(fname_raw)
runs.extend(([op.join(fname_raw + '/') + f for f in files if '.ds' in f]))
events_meg = list()
for run_number, this_run in enumerate(runs):
fname_raw = op.join(path_data, subject, this_run)
print(fname_raw)
raw = read_raw_ctf(fname_raw, preload=True, system_clock='ignore')
channel_trigger = np.where(np.array(raw.ch_names) == 'USPT001')[0][0]
# replace 255 values with 0
trigger_baseline = np.where(raw._data[channel_trigger, :] == 255)[0]
raw._data[channel_trigger, trigger_baseline] = 0.
# find triggers
events_meg_ = mne.find_events(raw)
# Add 48ms to the trigger events (according to delay with photodiod)
events_meg_ = np.array(events_meg_, float)
events_meg_[:, 0] += round(.048 * raw.info['sfreq'])
events_meg_ = np.array(events_meg_, int)
# to keep the run from which the event was found
events_meg_[:, 1] = run_number
events_meg.append(events_meg_)
# concatenate all meg events
events_meg = np.vstack(events_meg)
# add trigger index to meg_events
events_target = range(1, 126)
events_cue = range(126, 130)
events_probe = range(130, 141)
triggidx_array = []
for trigg in events_meg[:, 2]:
if trigg in events_target:
triggidx = 1
elif trigg in events_cue:
triggidx = 2
elif trigg in events_probe:
triggidx = 3
triggidx_array.append(triggidx)
events_meg = np.insert(events_meg, 3, triggidx_array, axis=1)
# Compare MEG and bhv triggers and save events_behavior for each event
event_type = 'Cue'
events_behavior_type = []
events_behavior_type = fix_triggers(events_meg,
events_behavior,
event_type='trigg' + event_type)
epochs_list = list()
# Read raw MEG, filter and epochs
for run_number, this_run in enumerate(runs):
fname_raw = op.join(path_data, subject, this_run)
print(fname_raw)
raw = read_raw_ctf(fname_raw, preload=True, system_clock='ignore')
events_meg_run = make_events_run(events_behavior_type, run_number)
event_id = {
'ttl_%i' % ii: ii
for ii in np.unique(events_meg_run[:, 2])
}
tmin = -.200
tmax = 1.500
epochs = Epochs(raw,
events_meg_run,
event_id=event_id,
tmin=tmin,
tmax=tmax,
preload=True,
baseline=None,
decim=10)
# Copy dev_head_t of the first run to others run
if run_number == 0:
dev_head_t = epochs.info['dev_head_t']
else:
epochs.info['dev_head_t'] = dev_head_t
# Apply baseline from beginning of epoch to t0
epochs.apply_baseline((-0.2, 0.))
epochs_list.append(epochs)
epochs = concatenate_epochs(epochs_list)
epochs.pick_types(meg=True, ref_meg=False)
events = events_behavior_type
events = complete_behavior(events)
return epochs, events