Compute power and phase lock in label of the source space

Compute time-frequency maps of power and phase lock in the source space. The inverse method is linear based on dSPM inverse operator.

The example also shows the difference in the time-frequency maps when they are computed with and without subtracting the evoked response from each epoch. The former results in induced activity only while the latter also includes evoked (stimulus-locked) activity.

# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_induced_power

print(__doc__)

Set parameters

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
label_name = 'Aud-rh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name

tmin, tmax, event_id = -0.2, 0.5, 2

# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.find_events(raw, stim_channel='STI 014')
inverse_operator = read_inverse_operator(fname_inv)

include = []
raw.info['bads'] += ['MEG 2443', 'EEG 053']  # bads + 2 more

# Picks MEG channels
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
                       stim=False, include=include, exclude='bads')
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)

# Load epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=reject,
                    preload=True)

# Compute a source estimate per frequency band including and excluding the
# evoked response
freqs = np.arange(7, 30, 2)  # define frequencies of interest
label = mne.read_label(fname_label)
n_cycles = freqs / 3.  # different number of cycle per frequency

# subtract the evoked response in order to exclude evoked activity
epochs_induced = epochs.copy().subtract_evoked()

plt.close('all')

for ii, (this_epochs, title) in enumerate(zip([epochs, epochs_induced],
                                              ['evoked + induced',
                                               'induced only'])):
    # compute the source space power and the inter-trial coherence
    power, itc = source_induced_power(
        this_epochs, inverse_operator, freqs, label, baseline=(-0.1, 0),
        baseline_mode='percent', n_cycles=n_cycles, n_jobs=1)

    power = np.mean(power, axis=0)  # average over sources
    itc = np.mean(itc, axis=0)  # average over sources
    times = epochs.times

    ##########################################################################
    # View time-frequency plots
    plt.subplots_adjust(0.1, 0.08, 0.96, 0.94, 0.2, 0.43)
    plt.subplot(2, 2, 2 * ii + 1)
    plt.imshow(20 * power,
               extent=[times[0], times[-1], freqs[0], freqs[-1]],
               aspect='auto', origin='lower', vmin=0., vmax=30., cmap='RdBu_r')
    plt.xlabel('Time (s)')
    plt.ylabel('Frequency (Hz)')
    plt.title('Power (%s)' % title)
    plt.colorbar()

    plt.subplot(2, 2, 2 * ii + 2)
    plt.imshow(itc,
               extent=[times[0], times[-1], freqs[0], freqs[-1]],
               aspect='auto', origin='lower', vmin=0, vmax=0.7,
               cmap='RdBu_r')
    plt.xlabel('Time (s)')
    plt.ylabel('Frequency (Hz)')
    plt.title('ITC (%s)' % title)
    plt.colorbar()

plt.show()

Out:

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif...
    Read a total of 3 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.
320 events found
Event IDs: [ 1  2  3  4  5 32]
Reading inverse operator decomposition from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif...
    Reading inverse operator info...
    [done]
    Reading inverse operator decomposition...
    [done]
    305 x 305 full covariance (kind = 1) found.
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Noise covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 2) found.
    Source covariance matrix read.
    22494 x 22494 diagonal covariance (kind = 6) found.
    Orientation priors read.
    22494 x 22494 diagonal covariance (kind = 5) found.
    Depth priors read.
    Did not find the desired covariance matrix (kind = 3)
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    Reading a source space...
    Computing patch statistics...
    Patch information added...
    Distance information added...
    [done]
    2 source spaces read
    Read a total of 4 projection items:
        PCA-v1 (1 x 102) active
        PCA-v2 (1 x 102) active
        PCA-v3 (1 x 102) active
        Average EEG reference (1 x 60) active
    Source spaces transformed to the inverse solution coordinate frame
Not setting metadata
Not setting metadata
73 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 projection items activated
Loading data for 73 events and 421 original time points ...
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on MAG : ['MEG 1711']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
14 bad epochs dropped
Subtracting Evoked from Epochs
    The following channels are not included in the subtraction: EOG 061
[done]
Preparing the inverse operator for use...
    Scaled noise and source covariance from nave = 1 to nave = 1
    Created the regularized inverter
    Created an SSP operator (subspace dimension = 3)
    Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
    Computing noise-normalization factors (dSPM)...
[done]
Picked 305 channels from the data
Computing inverse...
    Eigenleads need to be weighted ...
Reducing data rank 81 -> 81
Computing source power ...
Applying baseline correction (mode: percent)
Preparing the inverse operator for use...
    Scaled noise and source covariance from nave = 1 to nave = 1
    Created the regularized inverter
    Created an SSP operator (subspace dimension = 3)
    Created the whitener using a noise covariance matrix with rank 302 (3 small eigenvalues omitted)
    Computing noise-normalization factors (dSPM)...
[done]
Picked 305 channels from the data
Computing inverse...
    Eigenleads need to be weighted ...
Reducing data rank 81 -> 81
Computing source power ...
Applying baseline correction (mode: percent)

Estimated memory usage: 66 MB