Generate simulated evoked data#

Use simulate_sparse_stc() to simulate evoked data.

# Author: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#         Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.datasets import sample
from mne.time_frequency import fit_iir_model_raw
from mne.viz import plot_sparse_source_estimates
from mne.simulation import simulate_sparse_stc, simulate_evoked

print(__doc__)

Load real data as templates:

data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sample'
raw = mne.io.read_raw_fif(meg_path / 'sample_audvis_raw.fif')
proj = mne.read_proj(meg_path / 'sample_audvis_ecg-proj.fif')
raw.add_proj(proj)
raw.info['bads'] = ['MEG 2443', 'EEG 053']  # mark bad channels

fwd_fname = meg_path / 'sample_audvis-meg-eeg-oct-6-fwd.fif'
ave_fname = meg_path / 'sample_audvis-no-filter-ave.fif'
cov_fname = meg_path / 'sample_audvis-cov.fif'

fwd = mne.read_forward_solution(fwd_fname)
fwd = mne.pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info['bads'])
cov = mne.read_cov(cov_fname)
info = mne.io.read_info(ave_fname)

label_names = ['Aud-lh', 'Aud-rh']
labels = [mne.read_label(meg_path / 'labels' / f'{ln}.label')
          for ln in label_names]
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.
    Read a total of 6 projection items:
        ECG-planar-999--0.200-0.400-PCA-01 (1 x 203)  idle
        ECG-planar-999--0.200-0.400-PCA-02 (1 x 203)  idle
        ECG-axial-999--0.200-0.400-PCA-01 (1 x 102)  idle
        ECG-axial-999--0.200-0.400-PCA-02 (1 x 102)  idle
        ECG-eeg-999--0.200-0.400-PCA-01 (1 x 59)  idle
        ECG-eeg-999--0.200-0.400-PCA-02 (1 x 59)  idle
6 projection items deactivated
Reading forward solution from /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif...
    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
    Desired named matrix (kind = 3523) not available
    Read MEG forward solution (7498 sources, 306 channels, free orientations)
    Desired named matrix (kind = 3523) not available
    Read EEG forward solution (7498 sources, 60 channels, free orientations)
    Forward solutions combined: MEG, EEG
    Source spaces transformed to the forward solution coordinate frame
    364 out of 366 channels remain after picking
    366 x 366 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
    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

Generate source time courses from 2 dipoles and the correspond evoked data

times = np.arange(300, dtype=np.float64) / raw.info['sfreq'] - 0.1
rng = np.random.RandomState(42)


def data_fun(times):
    """Function to generate random source time courses"""
    return (50e-9 * np.sin(30. * times) *
            np.exp(- (times - 0.15 + 0.05 * rng.randn(1)) ** 2 / 0.01))


stc = simulate_sparse_stc(fwd['src'], n_dipoles=2, times=times,
                          random_state=42, labels=labels, data_fun=data_fun)

Generate noisy evoked data

picks = mne.pick_types(raw.info, meg=True, exclude='bads')
iir_filter = fit_iir_model_raw(raw, order=5, picks=picks, tmin=60, tmax=180)[1]
nave = 100  # simulate average of 100 epochs
evoked = simulate_evoked(fwd, stc, info, cov, nave=nave, use_cps=True,
                         iir_filter=iir_filter)
    Average patch normals will be employed in the rotation to the local surface coordinates....
    Converting to surface-based source orientations...
    [done]
Projecting source estimate to sensor space...
[done]
4 projection items deactivated
Created an SSP operator (subspace dimension = 4)
4 projection items activated
SSP projectors applied...

Plot

plot_sparse_source_estimates(fwd['src'], stc, bgcolor=(1, 1, 1),
                             opacity=0.5, high_resolution=True)

plt.figure()
plt.psd(evoked.data[0])

evoked.plot(time_unit='s')
  • simulate evoked data
  • simulate evoked data
  • EEG (59 channels), Gradiometers (203 channels), Magnetometers (102 channels)
simulate evoked data
Total number of active sources: 2

Total running time of the script: ( 0 minutes 9.634 seconds)

Estimated memory usage: 411 MB

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