def make_phase_shuffled_surrogates_epochs(epochs, check_power=False):
'''
Make surrogate epochs using sklearn. Destroy phase information in each trial by randomization.
The phases values are randomized in teh frequency domain.
Parameters
----------
Epochs Object.
Output
------
Surrogate Epochs object
'''
surrogate = epochs.copy()
surr = surrogate.get_data()
for trial in range(len(surrogate)):
for channel in range(len(surrogate.ch_names)):
surr[trial, channel, :] = randomize_phase(surr[trial, channel, :])
surrogate._data = surr
if check_power:
from mne.time_frequency import compute_epochs_psd
ps1, _ = compute_epochs_psd(epochs, epochs.picks)
ps2, _ = compute_epochs_psd(surrogate, surrogate.picks)
# np.array_equal does not pass the assertion, due to minor changes in power.
assert np.allclose(ps1,
ps2), 'The power content does not match. Error.'
return surrogate
def make_phase_shuffled_surrogates_epochs(epochs, check_power=False):
'''
Make surrogate epochs using sklearn. Destroy phase information in each trial by randomization.
The phases values are randomized in teh frequency domain.
Parameters
----------
Epochs Object.
Output
------
Surrogate Epochs object
'''
surrogate = epochs.copy()
surr = surrogate.get_data()
for trial in range(len(surrogate)):
for channel in range(len(surrogate.ch_names)):
surr[trial, channel, :] = randomize_phase(surr[trial, channel, :])
surrogate._data = surr
if check_power:
from mne.time_frequency import compute_epochs_psd
ps1, _ = compute_epochs_psd(epochs, epochs.picks)
ps2, _ = compute_epochs_psd(surrogate, surrogate.picks)
# np.array_equal does not pass the assertion, due to minor changes in power.
assert np.allclose(ps1, ps2), 'The power content does not match. Error.'
return surrogate
def make_fftsurr_epochs(epochs, check_power=False):
'''
Make surrogate epochs using sklearn. Destroy each trial by shuffling the phase information.
The shuffling is performed in the frequency domain only using fftsurr function from mlab.
Parameters
----------
Epochs Object.
Output
------
Surrogate Epochs object
'''
from matplotlib.mlab import fftsurr
surrogate = epochs.copy()
surr = surrogate.get_data()
for trial in range(len(surrogate)):
for channel in range(len(surrogate.ch_names)):
surr[trial, channel, :] = fftsurr(surr[trial, channel, :])
surrogate._data = surr
if check_power:
from mne.time_frequency import compute_epochs_psd
ps1, _ = compute_epochs_psd(epochs, epochs.picks)
ps2, _ = compute_epochs_psd(surrogate, surrogate.picks)
assert np.allclose(ps1,
ps2), 'The power content does not match. Error.'
return surrogate
def make_fftsurr_epochs(epochs, check_power=False):
'''
Make surrogate epochs using sklearn. Destroy each trial by shuffling the phase information.
The shuffling is performed in the frequency domain only using fftsurr function from mlab.
Parameters
----------
Epochs Object.
Output
------
Surrogate Epochs object
'''
from matplotlib.mlab import fftsurr
surrogate = epochs.copy()
surr = surrogate.get_data()
for trial in range(len(surrogate)):
for channel in range(len(surrogate.ch_names)):
surr[trial, channel, :] = fftsurr(surr[trial, channel, :])
surrogate._data = surr
if check_power:
from mne.time_frequency import compute_epochs_psd
ps1, _ = compute_epochs_psd(epochs, epochs.picks)
ps2, _ = compute_epochs_psd(surrogate, surrogate.picks)
assert np.allclose(ps1, ps2), 'The power content does not match. Error.'
return surrogate
def test_psd_epochs():
"""Test PSD estimation on epochs
"""
raw = io.Raw(raw_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='mag', eeg=False, stim=False,
exclude=exclude)
picks = picks[:2]
n_fft = 512 # the FFT size (n_fft). Ideally a power of 2
tmin, tmax, event_id = -0.5, 0.5, 1
include = []
raw.info['bads'] += ['MEG 2443'] # bads
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False, eog=True,
stim=False, include=include, exclude='bads')
events = read_events(event_fname)
epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
baseline=(None, 0),
reject=dict(grad=4000e-13, eog=150e-6), proj=False,
preload=True)
tmin_full, tmax_full = -1, 1
epochs_full = Epochs(raw, events[:10], event_id, tmax=tmax_full,
tmin=tmin_full, picks=picks,
baseline=(None, 0),
reject=dict(grad=4000e-13, eog=150e-6), proj=False,
preload=True)
picks = pick_types(epochs.info, meg='grad', eeg=False, eog=True,
stim=False, include=include, exclude='bads')
psds, freqs = compute_epochs_psd(epochs[:1], fmin=2, fmax=300,
n_fft=n_fft, picks=picks)
psds_t, freqs_t = compute_epochs_psd(epochs_full[:1], fmin=2, fmax=300,
tmin=tmin, tmax=tmax,
n_fft=n_fft, picks=picks)
# this one will fail if you add for example 0.1 to tmin
assert_array_almost_equal(psds, psds_t, 27)
psds_proj, _ = compute_epochs_psd(epochs[:1].apply_proj(), fmin=2,
fmax=300, n_fft=n_fft, picks=picks)
assert_array_almost_equal(psds, psds_proj)
assert_true(psds.shape == (1, len(picks), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
def test_psd_epochs():
"""Test PSD estimation on epochs
"""
raw = io.Raw(raw_fname)
exclude = raw.info['bads'] + ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = pick_types(raw.info, meg='mag', eeg=False, stim=False,
exclude=exclude)
picks = picks[:2]
n_fft = 128 # the FFT size (n_fft). Ideally a power of 2
tmin, tmax, event_id = -1, 1, 1
include = []
raw.info['bads'] += ['MEG 2443'] # bads
# picks MEG gradiometers
picks = pick_types(raw.info, meg='grad', eeg=False, eog=True,
stim=False, include=include, exclude='bads')
events = read_events(event_fname)
epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
baseline=(None, 0),
reject=dict(grad=4000e-13, eog=150e-6), proj=False,
preload=True)
picks = pick_types(epochs.info, meg='grad', eeg=False, eog=True,
stim=False, include=include, exclude='bads')
psds, freqs = compute_epochs_psd(epochs[:1], fmin=2, fmax=300, n_fft=n_fft,
picks=picks)
psds_proj, _ = compute_epochs_psd(epochs[:1].apply_proj(), fmin=2,
fmax=300, n_fft=n_fft, picks=picks)
assert_array_almost_equal(psds, psds_proj)
assert_true(psds.shape == (1, len(picks), len(freqs)))
assert_true(np.sum(freqs < 0) == 0)
assert_true(np.sum(psds < 0) == 0)
events = mne.read_events(event_fname)
tmin, tmax, event_id = -1., 1., 1
include = []
raw.info['bads'] += ['MEG 2443'] # bads
# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,
stim=False, include=include, exclude='bads')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, proj=True,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6))
n_fft = 256 # the FFT size. Ideally a power of 2
psds, freqs = compute_epochs_psd(epochs, fmin=2, fmax=200, n_fft=n_fft,
n_jobs=2)
# average psds and save psds from first trial separately
average_psds = psds.mean(0)
average_psds = 10 * np.log10(average_psds) # transform into dB
some_psds = 10 * np.log10(psds[12])
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10, 5))
fig.suptitle('Single trial power', fontsize=12)
freq_mask = freqs < 150
freqs = freqs[freq_mask]
cmap = 'RdBu_r'
fig, ax = plt.subplots(1)
with FieldTripClient(host='localhost', port=1972,
tmax=150, wait_max=10) as rt_client:
# get measurement info guessed by MNE-Python
raw_info = rt_client.get_measurement_info()
# select gradiometers
picks = mne.pick_types(raw_info, meg='grad', eeg=False, eog=True,
stim=False, include=[], exclude=bads)
n_fft = 256 # the FFT size. Ideally a power of 2
n_samples = 2048 # time window on which to compute FFT
for ii in range(20):
epoch = rt_client.get_data_as_epoch(n_samples=n_samples, picks=picks)
psd, freqs = compute_epochs_psd(epoch, fmin=2, fmax=200, n_fft=n_fft)
cmap = 'RdBu_r'
freq_mask = freqs < 150
freqs = freqs[freq_mask]
log_psd = 10 * np.log10(psd[0])
tmin = epoch.events[0][0] / raw_info['sfreq']
tmax = (epoch.events[0][0] + n_samples) / raw_info['sfreq']
if ii == 0:
im = ax.imshow(log_psd[:, freq_mask].T, aspect='auto',
origin='lower', cmap=cmap)
ax.set_yticks(np.arange(0, len(freqs), 10))
ax.set_yticklabels(freqs[::10].round(1))
exclude='bads')
epochs = mne.Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
proj=True,
baseline=(None, 0),
reject=dict(grad=4000e-13, eog=150e-6))
n_fft = 256 # the FFT size. Ideally a power of 2
psds, freqs = compute_epochs_psd(epochs,
fmin=2,
fmax=200,
n_fft=n_fft,
n_jobs=2)
# average psds and save psds from first trial separately
average_psds = psds.mean(0)
average_psds = 10 * np.log10(average_psds) # transform into dB
some_psds = 10 * np.log10(psds[12])
fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10, 5))
fig.suptitle('Single trial power', fontsize=12)
freq_mask = freqs < 150
freqs = freqs[freq_mask]
raw_info = rt_client.get_measurement_info()
# select gradiometers
picks = mne.pick_types(raw_info,
meg='grad',
eeg=False,
eog=True,
stim=False,
include=[],
exclude=bads)
n_fft = 256 # the FFT size. Ideally a power of 2
n_samples = 2048 # time window on which to compute FFT
for ii in range(20):
epoch = rt_client.get_data_as_epoch(n_samples=n_samples, picks=picks)
psd, freqs = compute_epochs_psd(epoch, fmin=2, fmax=200, n_fft=n_fft)
cmap = 'RdBu_r'
freq_mask = freqs < 150
freqs = freqs[freq_mask]
log_psd = 10 * np.log10(psd[0])
tmin = epoch.events[0][0] / raw_info['sfreq']
tmax = (epoch.events[0][0] + n_samples) / raw_info['sfreq']
if ii == 0:
im = ax.imshow(log_psd[:, freq_mask].T,
aspect='auto',
origin='lower',
cmap=cmap)
