Generate simulated raw data

This example generates raw data by repeating a desired source activation multiple times.

# Authors: Yousra Bekhti <yousra.bekhti@gmail.com>
#          Mark Wronkiewicz <wronk.mark@gmail.com>
#          Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne import read_source_spaces, find_events, Epochs, compute_covariance
from mne.datasets import sample
from mne.simulation import simulate_sparse_stc, simulate_raw

print(__doc__)

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
trans_fname = data_path + '/MEG/sample/sample_audvis_raw-trans.fif'
src_fname = data_path + '/subjects/sample/bem/sample-oct-6-src.fif'
bem_fname = (data_path +
             '/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif')

# Load real data as the template
raw = mne.io.read_raw_fif(raw_fname)
raw.set_eeg_reference(projection=True)
raw = raw.crop(0., 30.)  # 30 sec is enough

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.
Current compensation grade : 0
Adding average EEG reference projection.
1 projection items deactivated

Generate dipole time series

n_dipoles = 4  # number of dipoles to create
epoch_duration = 2.  # duration of each epoch/event
n = 0  # harmonic number


def data_fun(times):
    """Generate time-staggered sinusoids at harmonics of 10Hz"""
    global n
    n_samp = len(times)
    window = np.zeros(n_samp)
    start, stop = [int(ii * float(n_samp) / (2 * n_dipoles))
                   for ii in (2 * n, 2 * n + 1)]
    window[start:stop] = 1.
    n += 1
    data = 25e-9 * np.sin(2. * np.pi * 10. * n * times)
    data *= window
    return data


times = raw.times[:int(raw.info['sfreq'] * epoch_duration)]
src = read_source_spaces(src_fname)
stc = simulate_sparse_stc(src, n_dipoles=n_dipoles, times=times,
                          data_fun=data_fun, random_state=0)
# look at our source data
fig, ax = plt.subplots(1)
ax.plot(times, 1e9 * stc.data.T)
ax.set(ylabel='Amplitude (nAm)', xlabel='Time (sec)')
mne.viz.utils.plt_show()
../../_images/sphx_glr_plot_simulate_raw_data_001.png

Out:

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

Simulate raw data

raw_sim = simulate_raw(raw, stc, trans_fname, src, bem_fname, cov='simple',
                       iir_filter=[0.2, -0.2, 0.04], ecg=True, blink=True,
                       n_jobs=1, verbose=True)
raw_sim.plot()
../../_images/sphx_glr_plot_simulate_raw_data_002.png

Out:

Provided parameters will provide approximately 15 events
Setting up raw simulation: 1 position, "cos2" interpolation
Blinks simulated and trace stored on channel:     EOG 061
ECG simulated and trace not stored
Event information stored on channel:              STI 014
Setting up forward solutions
Computing gain matrix for transform #1/1
  Simulating data for 0.000-30.001 sec with 16 events
Done

Plot evoked data

events = find_events(raw_sim)  # only 1 pos, so event number == 1
epochs = Epochs(raw_sim, events, 1, -0.2, epoch_duration)
cov = compute_covariance(epochs, tmax=0., method='empirical',
                         verbose='error')  # quick calc
evoked = epochs.average()
evoked.plot_white(cov, time_unit='s')
../../_images/sphx_glr_plot_simulate_raw_data_003.png

Out:

Trigger channel has a non-zero initial value of 1 (consider using initial_event=True to detect this event)
Removing orphaned offset at the beginning of the file.
15 events found
Event IDs: [1]
15 matching events found
Applying baseline correction (mode: mean)
Not setting metadata
Created an SSP operator (subspace dimension = 4)
4 projection items activated
Created an SSP operator (subspace dimension = 1)
8 projection items activated
estimated rank (eeg): 58
8 projection items activated
estimated rank (grad): 203
Created an SSP operator (subspace dimension = 3)
8 projection items activated
estimated rank (mag): 99
    Created an SSP operator (subspace dimension = 4)
Setting small MEG eigenvalues to zero.
Not doing PCA for MEG.
Setting small EEG eigenvalues to zero.
Not doing PCA for EEG.
    Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)

Total running time of the script: ( 0 minutes 31.897 seconds)

Estimated memory usage: 842 MB

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