def test_ems():
"""Test event-matched spatial filters"""
raw = io.Raw(raw_fname, preload=False)
# create unequal number of events
events = read_events(event_name)
events[-2, 2] = 3
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
assert_raises(ValueError, compute_ems, epochs, ['aud_l', 'vis_l'])
epochs.equalize_event_counts(epochs.event_id, copy=False)
assert_raises(KeyError, compute_ems, epochs, ['blah', 'hahah'])
surrogates, filters, conditions = compute_ems(epochs)
assert_equal(list(set(conditions)), [1, 3])
events = read_events(event_name)
event_id2 = dict(aud_l=1, aud_r=2, vis_l=3)
epochs = Epochs(raw, events, event_id2, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs.equalize_event_counts(epochs.event_id, copy=False)
n_expected = sum([len(epochs[k]) for k in ['aud_l', 'vis_l']])
assert_raises(ValueError, compute_ems, epochs)
surrogates, filters, conditions = compute_ems(epochs, ['aud_r', 'vis_l'])
assert_equal(n_expected, len(surrogates))
assert_equal(n_expected, len(conditions))
assert_equal(list(set(conditions)), [2, 3])
raw.close()
def test_ems():
"""Test event-matched spatial filters"""
raw = io.read_raw_fif(raw_fname, preload=False)
# create unequal number of events
events = read_events(event_name)
events[-2, 2] = 3
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
assert_raises(ValueError, compute_ems, epochs, ['aud_l', 'vis_l'])
epochs = epochs.equalize_event_counts(epochs.event_id, copy=False)[0]
assert_raises(KeyError, compute_ems, epochs, ['blah', 'hahah'])
surrogates, filters, conditions = compute_ems(epochs)
assert_equal(list(set(conditions)), [1, 3])
events = read_events(event_name)
event_id2 = dict(aud_l=1, aud_r=2, vis_l=3)
epochs = Epochs(raw, events, event_id2, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True)
epochs = epochs.equalize_event_counts(epochs.event_id, copy=False)[0]
n_expected = sum([len(epochs[k]) for k in ['aud_l', 'vis_l']])
assert_raises(ValueError, compute_ems, epochs)
surrogates, filters, conditions = compute_ems(epochs, ['aud_r', 'vis_l'])
assert_equal(n_expected, len(surrogates))
assert_equal(n_expected, len(conditions))
assert_equal(list(set(conditions)), [2, 3])
raw.close()
aspect='auto',
extent=[epochs.times[0], epochs.times[-1], 1,
len(X_transform)],
cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
# Plot average response
plt.figure()
plt.title('Average EMS signal')
mappings = [(key, value) for key, value in event_ids.items()]
for key, value in mappings:
ems_ave = X_transform[y == value]
plt.plot(epochs.times, ems_ave.mean(0), label=key)
plt.xlabel('Time (ms)')
plt.ylabel('a.u.')
plt.legend(loc='best')
plt.show()
# Visualize spatial filters across time
evoked = EvokedArray(filters, epochs.info, tmin=epochs.tmin)
evoked.plot_topomap(time_unit='s', scalings=1)
#############################################################################
# Note that a similar transformation can be applied with `compute_ems`
# However, this function replicates Schurger et al's original paper, and thus
# applies the normalization outside a leave-one-out cross-validation, which we
# recommend not to do.
epochs.equalize_event_counts(event_ids)
X_transform, filters, classes = compute_ems(epochs)
plt.title('single trial surrogates')
plt.imshow(X_transform[y.argsort()], origin='lower', aspect='auto',
extent=[epochs.times[0], epochs.times[-1], 1, len(X_transform)],
cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
# Plot average response
plt.figure()
plt.title('Average EMS signal')
mappings = [(key, value) for key, value in event_ids.items()]
for key, value in mappings:
ems_ave = X_transform[y == value]
plt.plot(epochs.times, ems_ave.mean(0), label=key)
plt.xlabel('Time (ms)')
plt.ylabel('a.u.')
plt.legend(loc='best')
plt.show()
# Visualize spatial filters across time
evoked = EvokedArray(filters, epochs.info, tmin=epochs.tmin)
evoked.plot_topomap()
#############################################################################
# Note that a similar transformation can be applied with `compute_ems`
# However, this function replicates Schurger et al's original paper, and thus
# applies the normalization outside a leave-one-out cross-validation, which we
# recommend not to do.
epochs.equalize_event_counts(event_ids)
X_transform, filters, classes = compute_ems(epochs)
def test_ems():
"""Test event-matched spatial filters."""
from sklearn.model_selection import StratifiedKFold
raw = io.read_raw_fif(raw_fname, preload=False)
# create unequal number of events
events = read_events(event_name)
events[-2, 2] = 3
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
pytest.raises(ValueError, compute_ems, epochs, ['aud_l', 'vis_l'])
epochs.equalize_event_counts(epochs.event_id)
pytest.raises(KeyError, compute_ems, epochs, ['blah', 'hahah'])
surrogates, filters, conditions = compute_ems(epochs)
assert_equal(list(set(conditions)), [1, 3])
events = read_events(event_name)
event_id2 = dict(aud_l=1, aud_r=2, vis_l=3)
epochs = Epochs(raw,
events,
event_id2,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
epochs.equalize_event_counts(epochs.event_id)
n_expected = sum([len(epochs[k]) for k in ['aud_l', 'vis_l']])
pytest.raises(ValueError, compute_ems, epochs)
surrogates, filters, conditions = compute_ems(epochs, ['aud_r', 'vis_l'])
assert_equal(n_expected, len(surrogates))
assert_equal(n_expected, len(conditions))
assert_equal(list(set(conditions)), [2, 3])
# test compute_ems cv
epochs = epochs['aud_r', 'vis_l']
epochs.equalize_event_counts(epochs.event_id)
cv = StratifiedKFold(n_splits=3)
compute_ems(epochs, cv=cv)
compute_ems(epochs, cv=2)
pytest.raises(ValueError, compute_ems, epochs, cv='foo')
pytest.raises(ValueError, compute_ems, epochs, cv=len(epochs) + 1)
raw.close()
# EMS transformer, check that identical to compute_ems
X = epochs.get_data()
y = epochs.events[:, 2]
X = X / np.std(X) # X scaled outside cv in compute_ems
Xt, coefs = list(), list()
ems = EMS()
assert_equal(ems.__repr__(), '<EMS: not fitted.>')
# manual leave-one-out to avoid sklearn version problem
for test in range(len(y)):
train = np.setdiff1d(range(len(y)), np.atleast_1d(test))
ems.fit(X[train], y[train])
coefs.append(ems.filters_)
Xt.append(ems.transform(X[[test]]))
assert_equal(ems.__repr__(), '<EMS: fitted with 4 filters on 2 classes.>')
assert_array_almost_equal(filters, np.mean(coefs, axis=0))
assert_array_almost_equal(surrogates, np.vstack(Xt))
reject = dict(grad=4000e-13, eog=150e-6)
epochs = mne.Epochs(raw,
events,
event_ids,
tmin,
tmax,
picks=picks,
baseline=None,
reject=reject)
# Let's equalize the trial counts in each condition
epochs.equalize_event_counts(epochs.event_id, copy=False)
# compute surrogate time series
surrogates, filters, conditions = compute_ems(epochs, ['AudL', 'VisL'])
import matplotlib.pyplot as plt
times = epochs.times * 1e3
plt.figure()
plt.title('single trial surrogates')
plt.imshow(surrogates[conditions.argsort()],
origin='lower',
aspect='auto',
extent=[times[0], times[-1], 1,
len(surrogates)])
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
plt.figure()
def test_ems():
"""Test event-matched spatial filters"""
raw = io.read_raw_fif(raw_fname, preload=False, add_eeg_ref=False)
# create unequal number of events
events = read_events(event_name)
events[-2, 2] = 3
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')
picks = picks[1:13:3]
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, add_eeg_ref=False)
assert_raises(ValueError, compute_ems, epochs, ['aud_l', 'vis_l'])
epochs = epochs.equalize_event_counts(epochs.event_id, copy=False)[0]
assert_raises(KeyError, compute_ems, epochs, ['blah', 'hahah'])
surrogates, filters, conditions = compute_ems(epochs)
assert_equal(list(set(conditions)), [1, 3])
events = read_events(event_name)
event_id2 = dict(aud_l=1, aud_r=2, vis_l=3)
epochs = Epochs(raw, events, event_id2, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True, add_eeg_ref=False)
epochs = epochs.equalize_event_counts(epochs.event_id, copy=False)[0]
n_expected = sum([len(epochs[k]) for k in ['aud_l', 'vis_l']])
assert_raises(ValueError, compute_ems, epochs)
surrogates, filters, conditions = compute_ems(epochs, ['aud_r', 'vis_l'])
assert_equal(n_expected, len(surrogates))
assert_equal(n_expected, len(conditions))
assert_equal(list(set(conditions)), [2, 3])
# test compute_ems cv
epochs = epochs['aud_r', 'vis_l']
epochs.equalize_event_counts(epochs.event_id)
if check_version('sklearn', '0.18'):
from sklearn.model_selection import StratifiedKFold
cv = StratifiedKFold()
else:
from sklearn.cross_validation import StratifiedKFold
cv = StratifiedKFold(epochs.events[:, 2])
compute_ems(epochs, cv=cv)
compute_ems(epochs, cv=2)
assert_raises(ValueError, compute_ems, epochs, cv='foo')
assert_raises(ValueError, compute_ems, epochs, cv=len(epochs) + 1)
raw.close()
# EMS transformer, check that identical to compute_ems
X = epochs.get_data()
y = epochs.events[:, 2]
X = X / np.std(X) # X scaled outside cv in compute_ems
Xt, coefs = list(), list()
ems = EMS()
assert_equal(ems.__repr__(), '<EMS: not fitted.>')
# manual leave-one-out to avoid sklearn version problem
for test in range(len(y)):
train = np.setdiff1d(range(len(y)), test)
ems.fit(X[train], y[train])
coefs.append(ems.filters_)
Xt.append(ems.transform(X[[test]]))
assert_equal(ems.__repr__(), '<EMS: fitted with 4 filters on 2 classes.>')
assert_array_almost_equal(filters, np.mean(coefs, axis=0))
assert_array_almost_equal(surrogates, np.vstack(Xt))
raw.filter(1, 45)
events = mne.read_events(event_fname)
include = [] # or stim channels ['STI 014']
ch_type = "grad"
picks = mne.pick_types(raw.info, meg=ch_type, eeg=False, stim=False, eog=True, include=include, exclude="bads")
reject = dict(grad=4000e-13, eog=150e-6)
epochs = mne.Epochs(raw, events, event_ids, tmin, tmax, picks=picks, baseline=None, reject=reject)
# Let's equalize the trial counts in each condition
epochs.equalize_event_counts(epochs.event_id, copy=False)
# compute surrogate time series
surrogates, filters, conditions = compute_ems(epochs, ["AudL", "VisL"])
import matplotlib.pyplot as plt
times = epochs.times * 1e3
plt.figure()
plt.title("single trial surrogates")
plt.imshow(
surrogates[conditions.argsort()], origin="lower", aspect="auto", extent=[times[0], times[-1], 1, len(surrogates)]
)
plt.xlabel("Time (ms)")
plt.ylabel("Trials (reordered by condition)")
plt.figure()
plt.title("Average EMS signal")
include = [] # or stim channels ['STI 014']
ch_type = 'grad'
picks = mne.pick_types(raw.info, meg=ch_type, eeg=False, stim=False, eog=True,
include=include, exclude='bads')
reject = dict(grad=4000e-13, eog=150e-6)
epochs = mne.Epochs(raw, events, event_ids, tmin, tmax, picks=picks,
baseline=None, reject=reject)
# Let's equalize the trial counts in each condition
epochs.equalize_event_counts(epochs.event_id, copy=False)
# compute surrogate time series
surrogates, filters, conditions = compute_ems(epochs, ['AudL', 'VisL'])
times = epochs.times * 1e3
plt.figure()
plt.title('single trial surrogates')
plt.imshow(surrogates[conditions.argsort()], origin='lower', aspect='auto',
extent=[times[0], times[-1], 1, len(surrogates)],
cmap='RdBu_r')
plt.xlabel('Time (ms)')
plt.ylabel('Trials (reordered by condition)')
plt.figure()
plt.title('Average EMS signal')
mappings = [(k, v) for k, v in event_ids.items() if v in conditions]
for key, value in mappings:
