Generate simulated source data

This example illustrates how to use the mne.simulation.SourceSimulator class to generate source estimates and raw data. It is meant to be a brief introduction and only highlights the simplest use case.

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Out:

    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
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)
    MEG and EEG forward solutions combined
    Source spaces transformed to the forward solution coordinate frame
Reading labels from parcellation...
   read 1 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/lh.aparc.annot
   read 0 labels from /home/circleci/mne_data/MNE-sample-data/subjects/sample/label/rh.aparc.annot
Setting up raw simulation: 1 position, "cos2" interpolation
Event information stored on channel:              STI 014
    Interval 0.000-1.665 sec
Setting up forward solutions
Computing gain matrix for transform #1/1
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    Interval 0.000-1.665 sec
    10 STC iterations provided
Done
Adding noise to 366/376 channels (366 channels in cov)
50 events found
Event IDs: [1]
Not setting metadata
Not setting metadata
50 matching events found
Setting baseline interval to [-0.049948803289596964, 0.0] sec
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 4)
4 projection items activated

# Author: Kostiantyn Maksymenko <kostiantyn.maksymenko@gmail.com>
#         Samuel Deslauriers-Gauthier <sam.deslauriers@gmail.com>
#
# License: BSD (3-clause)

import os.path as op

import numpy as np

import mne
from mne.datasets import sample

print(__doc__)

# For this example, we will be using the information of the sample subject.
# This will download the data if it not already on your machine. We also set
# the subjects directory so we don't need to give it to functions.
data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
subject = 'sample'

# First, we get an info structure from the test subject.
evoked_fname = op.join(data_path, 'MEG', subject, 'sample_audvis-ave.fif')
info = mne.io.read_info(evoked_fname)
tstep = 1. / info['sfreq']

# To simulate sources, we also need a source space. It can be obtained from the
# forward solution of the sample subject.
fwd_fname = op.join(data_path, 'MEG', subject,
                    'sample_audvis-meg-eeg-oct-6-fwd.fif')
fwd = mne.read_forward_solution(fwd_fname)
src = fwd['src']

# To select a region to activate, we use the caudal middle frontal to grow
# a region of interest.
selected_label = mne.read_labels_from_annot(
    subject, regexp='caudalmiddlefrontal-lh', subjects_dir=subjects_dir)[0]
location = 'center'  # Use the center of the region as a seed.
extent = 10.  # Extent in mm of the region.
label = mne.label.select_sources(
    subject, selected_label, location=location, extent=extent,
    subjects_dir=subjects_dir)

# Define the time course of the activity for each source of the region to
# activate. Here we use a sine wave at 18 Hz with a peak amplitude
# of 10 nAm.
source_time_series = np.sin(2. * np.pi * 18. * np.arange(100) * tstep) * 10e-9

# Define when the activity occurs using events. The first column is the sample
# of the event, the second is not used, and the third is the event id. Here the
# events occur every 200 samples.
n_events = 50
events = np.zeros((n_events, 3), int)
events[:, 0] = 100 + 200 * np.arange(n_events)  # Events sample.
events[:, 2] = 1  # All events have the sample id.

# Create simulated source activity. Here we use a SourceSimulator whose
# add_data method is key. It specified where (label), what
# (source_time_series), and when (events) an event type will occur.
source_simulator = mne.simulation.SourceSimulator(src, tstep=tstep)
source_simulator.add_data(label, source_time_series, events)

# Project the source time series to sensor space and add some noise. The source
# simulator can be given directly to the simulate_raw function.
raw = mne.simulation.simulate_raw(info, source_simulator, forward=fwd)
cov = mne.make_ad_hoc_cov(raw.info)
mne.simulation.add_noise(raw, cov, iir_filter=[0.2, -0.2, 0.04])
raw.plot()

# Plot evoked data to get another view of the simulated raw data.
events = mne.find_events(raw)
epochs = mne.Epochs(raw, events, 1, tmin=-0.05, tmax=0.2)
evoked = epochs.average()
evoked.plot()

Estimated memory usage: 227 MB

Simulate raw data using subject anatomy Define target events based on time lag, plot evoked response