def test_epoch_eq():
"""Test epoch count equalization"""
epochs_1 = Epochs(raw, events, event_id, tmin, tmax, picks=picks)
epochs_2 = Epochs(raw, events, event_id_2, tmin, tmax, picks=picks)
assert_true(epochs_1.events.shape[0] != epochs_2.events.shape[0])
equalize_epoch_counts(epochs_1, epochs_2)
assert_true(epochs_1.events.shape[0] == epochs_2.events.shape[0])
preload=True)
event_id = 3 # L visual
epochs2 = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=reject,
preload=True)
# Equalize trial counts to eliminate bias (which would otherwise be
# introduced by the abs() performed below)
equalize_epoch_counts(epochs1, epochs2)
###############################################################################
# Transform to source space
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
snr = 3.0
lambda2 = 1.0 / snr**2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
inverse_operator = read_inverse_operator(fname_inv)
sample_vertices = [s['vertno'] for s in inverse_operator['src']]
# Let's average and compute inverse, resampling to speed things up
evoked1 = epochs1.average()
evoked1.resample(50)
condition1 = apply_inverse(evoked1, inverse_operator, lambda2, method)
###############################################################################
# Read epochs for all channels, removing a bad one
exclude = ['MEG 2443']
picks = fiff.pick_types(raw.info, meg=True, eog=True, exclude=exclude)
event_id = 1 # L auditory
reject = dict(grad=1000e-13, mag=4000e-15, eog=150e-6)
epochs1 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=reject, preload=True)
event_id = 3 # L visual
epochs2 = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=reject, preload=True)
# Equalize trial counts to eliminate bias (which would otherwise be
# introduced by the abs() performed below)
equalize_epoch_counts(epochs1, epochs2)
###############################################################################
# Transform to source space
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
snr = 3.0
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
inverse_operator = read_inverse_operator(fname_inv)
sample_vertices = [s['vertno'] for s in inverse_operator['src']]
# Let's average and compute inverse, resampling to speed things up
evoked1 = epochs1.average()
evoked1.resample(50)
condition1 = apply_inverse(evoked1, inverse_operator, lambda2, method)
