Generate simulated source data#
This example illustrates how to use the
class to generate source estimates and raw data. It is meant to be a brief
introduction and only highlights the simplest use case.
# Author: Kostiantyn Maksymenko <email@example.com> # Samuel Deslauriers-Gauthier <firstname.lastname@example.org> # # License: BSD-3-Clause # Copyright the MNE-Python contributors.
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.
First, we get an info structure from the test subject.
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
To simulate sources, we also need a source space. It can be obtained from the forward solution of the sample subject.
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
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 ) location = "center" # Use the center of the region as a seed. extent = 10.0 # Extent in mm of the region. label = mne.label.select_sources( subject, selected_label, location=location, extent=extent, subjects_dir=subjects_dir )
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
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.
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.
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.
Project the source time series to sensor space and add some noise. The source simulator can be given directly to the simulate_raw function.
Setting up raw simulation: 1 position, "cos2" interpolation Event information stored on channel: STI 014 Interval 0.000–1.665 s Setting up forward solutions Computing gain matrix for transform #1/1 Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s Interval 0.000–1.665 s 10 STC iterations provided [done] Adding noise to 366/376 channels (366 channels in cov)
Plot evoked data to get another view of the simulated raw data.
50 events found on stim channel STI 014 Event IDs:  Not setting metadata 50 matching events found Setting baseline interval to [-0.049948803289596964, 0.0] s Applying baseline correction (mode: mean) Created an SSP operator (subspace dimension = 4) 4 projection items activated
Total running time of the script: (0 minutes 10.883 seconds)
Estimated memory usage: 188 MB