EEG Pipeline Tutorial
In this notebook, we will demonstrate how to use the newly implemented EEG pipeline of CMP3, using the “VEPCON” dataset, available at https://openneuro.org/datasets/ds003505/versions/1.1.1.
It is important to note that CMP3 does not include preprocessing of EEG data, so it is expected that you have your data ready to be analyzed.
Important: Note that the skull-surfaces provided with the dataset (“bem”, see below) which are needed to create the head model are obtained from non-defaced MRIs. You will not be able to proceed with surfaces created from VEPCON dataset alone.
Setup instructions
If you want to reproduce all the results of this notebook on your side, a conda environment.yml file can be downloaded at the following link: EEG_tutorial_environment.yml. The original .ipynb notebook file can be downloaded at the following link: EEG_pipeline_tutorial.ipynb.
Once you have downloaded the conda environment file, install the environment py37cmp-eeg as follows:
$ conda create env -f /path/to/downloaded/EEG_tutorial_environment.yml
This will install all the packages needed to run this notebook including jupyter lab.
You can then activate it, go to the directory where you downloaded the EEG_pipeline_tutorial.ipynb, and launch jupyter lab as follows:
$ cd /directory/of/downloaded/EEG_pipeline_tutorial.ipynb
$ conda activate py37cmp-eeg
$ jupyter lab
You are ready to open and interact with the notebook!
Loading the python packages used in the notebook
[1]:
# General
import sys
import os
import argparse
import subprocess
import pdb
import pickle
import shutil
import json
from IPython.display import SVG, display
import warnings
# Dataset management
import datalad.api as dl
# Data/graph handling and visualization
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
# BIDS import
from bids import BIDSLayout
# MNE imports
import mne
import mne_connectivity as mnec
# CMP3 imports
import cmp.project
from cmp.info import __version__, __copyright__
from cmtklib.util import print_error, print_blue, print_warning
from cmtklib.bids.io import __nipype_directory__, __cartool_directory__, __eeglab_directory__, __cmp_directory__
# other
from EEG_tutorial_utils import create_trans_files
Loading the BIDS dataset
For demonstration, we are going to use the latest version of VEPCON dataset, available on Open Neuro that already contains outputs from Connectome Mapper v3.0.3 and Freesurfer 7.1.1. A full description of the dataset can be found in Pascucci, Tourbier, et al. 2022.
In case you want to rerun the notebook, make sure to remove any ds003505_demo folder in the directory of the notebook. Otherwise, datalad install will complain.
[2]:
%%time
# Download example dataset with datalad
bids_dir = os.path.join(".", "ds003505_demo") # Adjust path to your BIDS dataset as needed
vepcon_data = dl.install(
path=bids_dir,
source="git@github.com:OpenNeuroDatasets/ds003505.git"
)
[INFO] Cloning dataset to Dataset(/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo)
[INFO] Attempting to clone from git@github.com:OpenNeuroDatasets/ds003505.git to /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo
[INFO] Start enumerating objects
[INFO] Start counting objects
[INFO] Start compressing objects
[INFO] Start receiving objects
[INFO] Start resolving deltas
[INFO] Completed clone attempts for Dataset(/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo)
[INFO] scanning for annexed files (this may take some time)
[INFO] Unable to parse git config from origin
[INFO] Remote origin does not have git-annex installed; setting annex-ignore
[INFO] This could be a problem with the git-annex installation on the remote. Please make sure that git-annex-shell is available in PATH when you ssh into the remote. Once you have fixed the git-annex installation, run: git annex enableremote origin
[INFO] access to 1 dataset sibling s3-PRIVATE not auto-enabled, enable with:
| datalad siblings -d "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo" enable -s s3-PRIVATE
CPU times: user 157 ms, sys: 148 ms, total: 304 ms
Wall time: 10.6 s
Running the EEG pipeline
As of now, the EEG pipeline can only be run directly from the application programming interface (API) as demonstrated in this notebook. As soon as possible, we will integrate it into the graphical user interface (GUI) and the command line interface (CLI).
First, we need to configure the following user-defined arguments. Please modify them as needed.
[3]:
# Adjust path to your BIDS dataset as needed
bids_dir = vepcon_data.path
# Adjust path of the output directory as needed
output_dir = os.path.join(bids_dir, 'derivatives')
# Adjust the subject to be processed as needed
participant_label = 'sub-01'
# Adjust path to the anatomical pipeline configuration file as needed
anat_pipeline_config = os.path.join(bids_dir, 'code', 'ConnectomeMapper-Docker', 'ref_anatomical_config.json')
# Adjust path to the MNE-based pipeline configuration file as needed
eeg_pipeline_config = os.path.join('.', 'ref_mne_eeg_config.json')
The eeg pipeline config .json file contains information that CMP3 needs to correctly load EEG data and associated information like electrode positions, names of conditions, which parcellation to use, etc. as seen below:
[4]:
%cat ref_mne_eeg_config.json
{
"Global": {
"process_type": "eeg",
"subjects": [
"sub-01"
],
"subject": "sub-01",
"version": "v3.0.3"
},
"eeg_preparer_stage": {
"eeg_format": ".set",
"epochs": "sub-01_task-faces_desc-preproc_eeg.set",
"invsol_format": "mne-sLORETA",
"parcellation": {
"atlas": "lausanne2018",
"res": "scale1"
},
"number_of_threads": 1,
"EEG_params": {
"expe_name": "faces",
"EEG_event_IDs": {
"SCRAMBLED" : 0,
"FACES" : 1
},
"start_t": -0.2,
"end_t" : 0.6
}
},
"eeg_inverse_solution_stage": {
"eeg_format": ".set",
"number_of_threads": 1
},
"Multi-processing": {
"number_of_cores": 1
}
}
Note: If you would like to run another subject (all available subjects except subjects 5 and 15 can be run), you will need to modify the config files (replacing sub-<label> accordingly).
Then, we need to tell datalad to download the actual content of the structural MRI and EEG files that will be input to the pipelines.
[5]:
%%time
# Raw MRI
vepcon_data.get(f'{participant_label}/anat/')
# CMP3 and Frresurfer derivatives
vepcon_data.get(f'derivatives/cmp-v3.0.3/{participant_label}/anat/')
vepcon_data.get(f'derivatives/freesurfer-7.1.1/{participant_label}/')
# Electrode position
vepcon_data.get(f'derivatives/{__cartool_directory__}/{participant_label}/' +
f'eeg/{participant_label}_eeg.xyz')
# Preprocessed EEG in EEGLab .fdt/.set format
vepcon_data.get(f'derivatives/{__eeglab_directory__}/{participant_label}/eeg/' +
f'{participant_label}_task-faces_desc-preproc_eeg.fdt')
vepcon_data.get(f'derivatives/{__eeglab_directory__}/{participant_label}/eeg/' +
f'{participant_label}_task-faces_desc-preproc_eeg.set')
CPU times: user 1.38 s, sys: 273 ms, total: 1.65 s
Wall time: 3min 58s
[5]:
[{'type': 'file',
'refds': '/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo',
'status': 'ok',
'path': '/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set',
'action': 'get',
'annexkey': 'MD5E-s3318272--2421186ff914211599affe7be30985ba.set',
'message': 'from s3-PUBLIC...'}]
Once the files are available, we need to configure a new CMP3 project.
[6]:
# initialize project
project = cmp.project.ProjectInfo()
project.base_directory = os.path.abspath(bids_dir)
project.output_directory = os.path.abspath(output_dir)
project.subjects = ["{}".format(participant_label)]
project.subject = "{}".format(participant_label)
# VEPCON dataset does not have a subject/sessions structure
project.subject_sessions = [""]
project.subject_session = ""
# Set the path to the anatomical pipeline configurration file
project.anat_config_file = os.path.abspath(anat_pipeline_config)
As the dataset is in BIDS, we can use Pybids to help us with the task of interacting with the files of the dataset.
[7]:
# Represent the BIDS dataset as a PyBIDS BIDSLayout
bids_layout = BIDSLayout(project.base_directory)
# Add derivative folder containing the connectivity matrices
bids_layout.add_derivatives(os.path.join(project.base_directory, "derivatives", "cmp-v3.0.3"))
Once set, we can run the anatomical pipeline, in order to obtain, among other things, Freesurfer derivatives necessary for the MNE pipeline.
Freesurfer and CMP3 derivatives are indeed provided with the VEPCON dataset, so we do not need to run it, but if run on a fresh dataset, it can take several hours (per subject).
[8]:
%%time
# Do not run again the anatomical pipeline
# You will have to set it to True on a fresh dataset
run = False
# Initialize the anatomical pipeline reading the configuration file
anat_pipeline = cmp.project.init_anat_project(project, False)
if anat_pipeline is not None:
# Check if inputs to anatomical pipeline are valid
anat_valid_inputs = anat_pipeline.check_input(bids_layout, gui=False)
if anat_valid_inputs:
if run:
print(">> Process anatomical pipeline")
anat_pipeline.process()
else:
print_error(" .. ERROR: Invalid inputs")
exit_code = 1
# Check if outputs to anatomical pipeline are valid
anat_valid_outputs, msg = anat_pipeline.check_output()
# Set the freesurfer subjects directory and the subject id
project.freesurfer_subjects_dir = anat_pipeline.stages['Segmentation'].config.freesurfer_subjects_dir
project.freesurfer_subject_id = anat_pipeline.stages['Segmentation'].config.freesurfer_subject_id
Created directory /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01
.. LOAD: Load anatomical config file : /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/code/ConnectomeMapper-Docker/ref_anatomical_config.json
.. WARNING: CMP3 version used to generate the configuration files (v3.0.2) and version of CMP3 used (v3.0.3) differ
**** Check Inputs ****
> Looking in /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo for....
/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/anat/sub-01_desc-brain_T1w.nii.gz
... t1_file : /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/anat/sub-01_desc-brain_T1w.nii.gz
/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/anat/sub-01_desc-cmp_T1w.json
... t1_json_file : /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/anat/sub-01_desc-cmp_T1w.json
Inputs check finished successfully.
Only anatomical data (T1) available.
.. INFO: Valid derivatives for anatomical pipeline
CPU times: user 57.5 ms, sys: 4.16 ms, total: 61.7 ms
Wall time: 57 ms
[9]:
project.freesurfer_subjects_dir
[9]:
'/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1'
In VEPCON, the electrode positions are provided in a file in the Cartool-derivatives folder, but CMP3 expects them in the EEGLAB-derivatives folder.
[10]:
# Copy the file to the appropriate location
cartool_file_location = os.path.join(
bids_dir, 'derivatives', __cartool_directory__,
participant_label,'eeg', participant_label + '_eeg.xyz'
)
eeglab_file_location = os.path.join(
bids_dir, 'derivatives', 'eeglab-v14.1.1',
participant_label, 'eeg', participant_label + '_eeg.xyz')
if not os.path.exists(eeglab_file_location):
_ = shutil.copyfile(cartool_file_location, eeglab_file_location)
Since we are using non-defaced MRIs, which are not exactly the same as the ones provided on OpenNeuro, we need an additional transform that will be applied to the electrode positions.
[11]:
# The following line creates the appropriate file with this transform in derivatives/cmp-v3.0.3:
create_trans_files(bids_dir, participant_label)
Finally, you can run the EEG pipeline.
[12]:
%%time
# Set the path to the EEG pipeline configurration file
project.eeg_config_file = os.path.abspath(eeg_pipeline_config)
if anat_valid_outputs:
# Initialize the EEG pipeline reading the configuration file and check input validity
eeg_valid_inputs, eeg_pipeline = cmp.project.init_eeg_project(project, bids_layout, False)
if eeg_pipeline is not None:
eeg_pipeline.parcellation_scheme = anat_pipeline.parcellation_scheme
eeg_pipeline.atlas_info = anat_pipeline.atlas_info
if eeg_valid_inputs:
print(">> Process EEG pipeline")
eeg_pipeline.process()
else:
print(" .. ERROR: Invalid inputs")
exit_code = 1
else:
print_error(f' .. ERROR: Invalid anatomical outputs for eeg pipeline')
print_error(f'{msg}')
exit_code = 1
**** Check Inputs is still not implemented ****
.. LOAD: Load eeg config file : /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ref_mne_eeg_config.json
.. INFO: Generated with the same CMP3 version
>> Process EEG pipeline
220221-10:54:42,107 nipype.interface INFO:
**** Processing ****
Parcellation atlas: lausanne2018
Parcellation resolution: scale1
220221-10:54:42,447 nipype.workflow INFO:
Generated workflow graph: /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/graph.svg (graph2use=colored, simple_form=True).
220221-10:54:42,469 nipype.workflow INFO:
Workflow eeg_pipeline settings: ['check', 'execution', 'logging', 'monitoring']
220221-10:54:42,480 nipype.workflow INFO:
Running in parallel.
220221-10:54:42,483 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-10:54:42,609 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_preparer_stage.eeglab2fif" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preparer_stage/eeglab2fif".
220221-10:54:42,614 nipype.workflow INFO:
[Node] Executing "eeglab2fif" <cmtklib.interfaces.mne.EEGLAB2fif>
Extracting parameters from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set...
Not setting metadata
Not setting metadata
588 matching events found
No baseline correction applied
0 projection items activated
Ready.
Applying baseline correction (mode: mean)
Adding average EEG reference projection.
1 projection items deactivated
Average reference projection was added, but has not been applied yet. Use the apply_proj method to apply it.
220221-10:54:43,765 nipype.workflow INFO:
[Node] Finished "eeglab2fif", elapsed time 1.148652s.
220221-10:54:44,486 nipype.workflow INFO:
[Job 0] Completed (eeg_pipeline.eeg_preparer_stage.eeglab2fif).
220221-10:54:44,492 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 2 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-10:54:44,677 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_preparer_stage.createsrc" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preparer_stage/createsrc".
220221-10:54:44,681 nipype.workflow INFO:
[Node] Executing "createsrc" <cmtklib.interfaces.mne.CreateSrc>
Setting up the source space with the following parameters:
SUBJECTS_DIR = /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1
Subject = sub-01
Surface = white
Octahedron subdivision grade 6
>>> 1. Creating the source space...
Doing the octahedral vertex picking...
Loading /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/lh.white...
Mapping lh sub-01 -> oct (6) ...
Triangle neighbors and vertex normals...
Loading geometry from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/lh.sphere...
Setting up the triangulation for the decimated surface...
loaded lh.white 4098/149863 selected to source space (oct = 6)
Loading /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/rh.white...
Mapping rh sub-01 -> oct (6) ...
Triangle neighbors and vertex normals...
Loading geometry from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/surf/rh.sphere...
Setting up the triangulation for the decimated surface...
loaded rh.white 4098/147183 selected to source space (oct = 6)
Calculating source space distances (limit=inf mm)...
220221-10:54:46,488 nipype.workflow INFO:
[MultiProc] Running 1 tasks, and 1 jobs ready. Free memory (GB): 28.00/28.20, Free processors: 0/1.
Currently running:
* eeg_pipeline.eeg_preparer_stage.createsrc
Computing patch statistics...
Patch information added...
Computing patch statistics...
Patch information added...
You are now one step closer to computing the gain matrix
Write a source space...
[done]
Write a source space...
[done]
2 source spaces written
220221-11:03:00,105 nipype.workflow INFO:
[Node] Finished "createsrc", elapsed time 495.422403s.
220221-11:03:00,974 nipype.workflow INFO:
[Job 1] Completed (eeg_pipeline.eeg_preparer_stage.createsrc).
220221-11:03:00,979 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 2 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-11:03:01,116 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_inverse_solution_stage.createcov" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_inverse_solution_stage/createcov".
220221-11:03:01,121 nipype.workflow INFO:
[Node] Executing "createcov" <cmtklib.interfaces.mne.CreateCov>
Reading /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_epo.fif ...
Read a total of 1 projection items:
Average EEG reference (1 x 128) idle
Found the data of interest:
t = -200.00 ... 600.00 ms
0 CTF compensation matrices available
Not setting metadata
Not setting metadata
588 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 1)
1 projection items activated
Computing rank from data with rank=None
Using tolerance 3.2e-11 (2.2e-16 eps * 128 dim * 1.1e+03 max singular value)
Estimated rank (eeg): 127
EEG: rank 127 computed from 128 data channels with 1 projector
Created an SSP operator (subspace dimension = 1)
Setting small EEG eigenvalues to zero (without PCA)
Reducing data rank from 128 -> 127
Estimating covariance using SHRUNK
220221-11:03:02,977 nipype.workflow INFO:
[MultiProc] Running 1 tasks, and 1 jobs ready. Free memory (GB): 28.00/28.20, Free processors: 0/1.
Currently running:
* eeg_pipeline.eeg_inverse_solution_stage.createcov
Done.
Estimating covariance using EMPIRICAL
Done.
Using cross-validation to select the best estimator.
Number of samples used : 29988
log-likelihood on unseen data (descending order):
shrunk: 71.788
empirical: -373.964
selecting best estimator: shrunk
[done]
220221-11:03:06,998 nipype.workflow INFO:
[Node] Finished "createcov", elapsed time 5.873837s.
220221-11:03:08,982 nipype.workflow INFO:
[Job 2] Completed (eeg_pipeline.eeg_inverse_solution_stage.createcov).
220221-11:03:08,984 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-11:03:09,119 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_preparer_stage.createbem" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_preparer_stage/createbem".
220221-11:03:09,126 nipype.workflow INFO:
[Node] Executing "createbem" <cmtklib.interfaces.mne.CreateBEM>
Creating the BEM geometry...
Going from 5th to 4th subdivision of an icosahedron (n_tri: 20480 -> 5120)
Going from 5th to 4th subdivision of an icosahedron (n_tri: 20480 -> 5120)
Going from 5th to 4th subdivision of an icosahedron (n_tri: 20480 -> 5120)
outer skin CM is -1.05 -8.88 11.57 mm
outer skull CM is -1.05 -8.80 11.09 mm
inner skull CM is -1.05 -10.32 19.78 mm
Checking that surface outer skull is inside surface outer skin ...
220221-11:03:10,985 nipype.workflow INFO:
[MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 28.00/28.20, Free processors: 0/1.
Currently running:
* eeg_pipeline.eeg_preparer_stage.createbem
Checking that surface inner skull is inside surface outer skull ...
Checking distance between outer skin and outer skull surfaces...
Minimum distance between the outer skin and outer skull surfaces is approximately 1.6 mm
Checking distance between outer skull and inner skull surfaces...
Minimum distance between the outer skull and inner skull surfaces is approximately 1.8 mm
Surfaces passed the basic topology checks.
Complete.
Approximation method : Linear collocation
Three-layer model surfaces loaded.
Computing the linear collocation solution...
Matrix coefficients...
outer skin (2562) -> outer skin (2562) ...
outer skin (2562) -> outer skull (2562) ...
outer skin (2562) -> inner skull (2562) ...
outer skull (2562) -> outer skin (2562) ...
outer skull (2562) -> outer skull (2562) ...
outer skull (2562) -> inner skull (2562) ...
inner skull (2562) -> outer skin (2562) ...
inner skull (2562) -> outer skull (2562) ...
inner skull (2562) -> inner skull (2562) ...
Inverting the coefficient matrix...
IP approach required...
Matrix coefficients (homog)...
inner skull (2562) -> inner skull (2562) ...
Inverting the coefficient matrix (homog)...
Modify the original solution to incorporate IP approach...
Combining...
Scaling...
Solution ready.
BEM geometry computations complete.
220221-11:04:28,211 nipype.workflow INFO:
[Node] Finished "createbem", elapsed time 79.081617s.
220221-11:04:29,61 nipype.workflow INFO:
[Job 3] Completed (eeg_pipeline.eeg_preparer_stage.createbem).
220221-11:04:29,65 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-11:04:29,195 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_loader_stage.eegloader" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_loader_stage/eegloader".
220221-11:04:29,198 nipype.workflow INFO:
[Node] Executing "eegloader" <cmtklib.interfaces.eeg.EEGLoader>
220221-11:04:31,63 nipype.workflow INFO:
[MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 28.00/28.20, Free processors: 0/1.
Currently running:
* eeg_pipeline.eeg_loader_stage.eegloader
220221-11:04:37,177 nipype.workflow INFO:
[Node] Finished "eegloader", elapsed time 7.977005s.
220221-11:04:39,71 nipype.workflow INFO:
[Job 4] Completed (eeg_pipeline.eeg_loader_stage.eegloader).
220221-11:04:39,76 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-11:04:39,217 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_inverse_solution_stage.createfwd" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_inverse_solution_stage/createfwd".
220221-11:04:39,222 nipype.workflow INFO:
[Node] Executing "createfwd" <cmtklib.interfaces.mne.CreateFwd>
Reading /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_epo.fif ...
Read a total of 1 projection items:
Average EEG reference (1 x 128) idle
Found the data of interest:
t = -200.00 ... 600.00 ms
0 CTF compensation matrices available
Not setting metadata
Not setting metadata
588 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 1)
1 projection items activated
Source space : /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_src.fif
MRI -> head transform : /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_trans.fif
Measurement data : instance of Info
Conductor model : /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_bem.fif
Accurate field computations
Do computations in head coordinates
Free source orientations
Reading /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_src.fif...
Read 2 source spaces a total of 8196 active source locations
Coordinate transformation: MRI (surface RAS) -> head
1.000000 0.000000 0.000000 0.00 mm
0.000000 1.000000 0.000000 9.00 mm
0.000000 0.000000 1.000000 -11.00 mm
0.000000 0.000000 0.000000 1.00
Read 128 EEG channels from info
Head coordinate coil definitions created.
Source spaces are now in head coordinates.
Setting up the BEM model using /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_bem.fif...
Loading surfaces...
Loading the solution matrix...
Three-layer model surfaces loaded.
Loaded linear_collocation BEM solution from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_bem.fif
Employing the head->MRI coordinate transform with the BEM model.
BEM model sub-01_bem.fif is now set up
Source spaces are in head coordinates.
Checking that the sources are inside the surface (will take a few...)
Skipping interior check for 1736 sources that fit inside a sphere of radius 53.7 mm
Skipping solid angle check for 0 points using Qhull
[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.
220221-11:04:41,74 nipype.workflow INFO:
[MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 28.00/28.20, Free processors: 0/1.
Currently running:
* eeg_pipeline.eeg_inverse_solution_stage.createfwd
[Parallel(n_jobs=4)]: Done 2 out of 4 | elapsed: 6.6s remaining: 6.6s
[Parallel(n_jobs=4)]: Done 4 out of 4 | elapsed: 6.6s remaining: 0.0s
[Parallel(n_jobs=4)]: Done 4 out of 4 | elapsed: 6.6s finished
Skipping interior check for 1721 sources that fit inside a sphere of radius 53.7 mm
Skipping solid angle check for 0 points using Qhull
[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 2 out of 4 | elapsed: 0.3s remaining: 0.3s
[Parallel(n_jobs=4)]: Done 4 out of 4 | elapsed: 0.4s remaining: 0.0s
[Parallel(n_jobs=4)]: Done 4 out of 4 | elapsed: 0.4s finished
Setting up for EEG...
Computing EEG at 8196 source locations (free orientations)...
[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 2 out of 4 | elapsed: 1.6s remaining: 1.6s
[Parallel(n_jobs=4)]: Done 4 out of 4 | elapsed: 1.7s remaining: 0.0s
[Parallel(n_jobs=4)]: Done 4 out of 4 | elapsed: 1.7s finished
Finished.
Write a source space...
[done]
Write a source space...
[done]
2 source spaces written
220221-11:04:50,779 nipype.workflow INFO:
[Node] Finished "createfwd", elapsed time 11.554622s.
220221-11:04:51,82 nipype.workflow INFO:
[Job 5] Completed (eeg_pipeline.eeg_inverse_solution_stage.createfwd).
220221-11:04:51,87 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-11:04:51,217 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_inverse_solution_stage.invsol" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_inverse_solution_stage/invsol".
220221-11:04:51,227 nipype.workflow INFO:
[Node] Executing "invsol" <cmtklib.interfaces.mne.MNEInverseSolution>
Reading /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_epo.fif ...
Read a total of 1 projection items:
Average EEG reference (1 x 128) idle
Found the data of interest:
t = -200.00 ... 600.00 ms
0 CTF compensation matrices available
Not setting metadata
Not setting metadata
588 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 1)
1 projection items activated
Reading forward solution from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_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 EEG forward solution (8196 sources, 128 channels, free orientations)
Source spaces transformed to the forward solution coordinate frame
128 x 128 full covariance (kind = 1) found.
Read a total of 1 projection items:
Average EEG reference (1 x 128) active
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
Computing inverse operator with 128 channels.
128 out of 128 channels remain after picking
Selected 128 channels
Whitening the forward solution.
Created an SSP operator (subspace dimension = 1)
Computing rank from covariance with rank=None
Using tolerance 1.2e-14 (2.2e-16 eps * 128 dim * 0.43 max singular value)
220221-11:04:53,84 nipype.workflow INFO:
[MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 28.00/28.20, Free processors: 0/1.
Currently running:
* eeg_pipeline.eeg_inverse_solution_stage.invsol
Estimated rank (eeg): 127
EEG: rank 127 computed from 128 data channels with 1 projector
Setting small EEG eigenvalues to zero (without PCA)
Creating the source covariance matrix
Adjusting source covariance matrix.
Computing SVD of whitened and weighted lead field matrix.
largest singular value = 11.2136
scaling factor to adjust the trace = 4.17725e+26 (nchan = 128 nzero = 1)
Write inverse operator decomposition in /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_inv.fif...
Write a source space...
[done]
Write a source space...
[done]
2 source spaces written
Writing inverse operator info...
Writing noise covariance matrix.
Writing source covariance matrix.
Writing orientation priors.
[done]
Preparing the inverse operator for use...
Scaled noise and source covariance from nave = 1 to nave = 588
Created the regularized inverter
Created an SSP operator (subspace dimension = 1)
Created the whitener using a noise covariance matrix with rank 127 (1 small eigenvalues omitted)
Computing noise-normalization factors (sLORETA)...
[done]
Picked 128 channels from the data
Computing inverse...
Eigenleads need to be weighted ...
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[done]
Reading labels from parcellation...
read 35 labels from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/label/lh.lausanne2018.scale1.annot
read 35 labels from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/label/rh.lausanne2018.scale1.annot
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
Extracting time courses for 70 labels (mode: pca_flip)
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220221-11:12:20,854 nipype.workflow INFO:
[Node] Finished "invsol", elapsed time 449.62434s.
220221-11:12:21,516 nipype.workflow INFO:
[Job 6] Completed (eeg_pipeline.eeg_inverse_solution_stage.invsol).
220221-11:12:21,523 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-11:12:21,751 nipype.workflow INFO:
[Node] Setting-up "eeg_pipeline.eeg_datasinker" in "/home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/nipype-1.7.0/sub-01/eeg_pipeline/eeg_datasinker".
220221-11:12:21,758 nipype.workflow INFO:
[Node] Executing "eeg_datasinker" <nipype.interfaces.io.DataSink>
220221-11:12:21,761 nipype.workflow INFO:
[Node] Finished "eeg_datasinker", elapsed time 0.000549s.
220221-11:12:23,518 nipype.workflow INFO:
[Job 7] Completed (eeg_pipeline.eeg_datasinker).
220221-11:12:23,523 nipype.workflow INFO:
[MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 28.20/28.20, Free processors: 1/1.
220221-11:12:25,653 nipype.interface INFO:
**** Processing finished ****
CPU times: user 3.85 s, sys: 924 ms, total: 4.78 s
Wall time: 17min 43s
/home/localadmin/Softwares/miniconda3/envs/py37cmp-eeg/lib/python3.7/site-packages/joblib/externals/loky/backend/resource_tracker.py:320: UserWarning: resource_tracker: There appear to be 3 leaked folder objects to clean up at shutdown
(len(rtype_registry), rtype))
A closer look at the EEG pipeline outputs
Let’s have a closer look at the outputs that the EEG pipeline produces.
First of all: Connectomemapper works in such a way that the pipeline is first assembled and only afterwards, it is executed. During the assembly stage, input and output variables are connected and CMP3 produces a graph that visualizes this.
[13]:
%matplotlib inline
path_to_svg = os.path.join(output_dir,__nipype_directory__,participant_label,'eeg_pipeline','graph.svg')
display(SVG(filename=path_to_svg))
Apart from the input (“datasource”) and output (“eeg sinker”) nodes that are apart, you can see three blue boxes that represent the stages of the pipeline flow:
* Preparer stage
* Loader stage
* Inverse solution stage
Each of the stages, again, has an input and and output node, as well as several nodes representing processing steps. Each processing step has its own “interface” which you can find in cmtklib/interfaces (“mne” in parentheses indicates that they are defined in the file mne.py).
In the following, we will go over the interfaces and show what output they produce.
Preparer stage
Let’s first have a look at the information given by the config file regarding this stage:
[...]
"eeg_preparer_stage": {
"eeg_format": ".set",
"epochs": "sub-01_task-faces_desc-preproc_eeg.set",
"invsol_format": "mne-sLORETA",
"parcellation": {
"label": "lausanne2008",
"desc": "",
"suffix": "scale1"
},
"number_of_threads": 1,
"EEG_params": {
"expe_name": "faces",
"EEG_event_IDs": {
"SCRAMBLED" : 0,
"FACES" : 1
},
"start_t": -0.2,
"end_t" : 0.6
}
},
[...]
The preparer stage has three processing steps:
eeglab2fif: reads eeglab data and converts them to MNE format (.fif file extension)createsrc: creates the dipole locations along the surface of the braincreatebem: creates the boundary element method
eeglab2fif
If your data are not already in MNE format (.fif file extension), they have to be read and re-saved. The eeglab2fif interface does this for EEGLAB-format data (.set file extension). The interface produces a file named sub-01_epo.fif in the derivatives/cmp-v3.0.3 folder. Critically, the saved epochs contain a montage, i.e. the sensor locations which have to be supplied in a file names sub-01.xyz inside the subject’s EEGLAB derivatives folder
(derivatives/eeglab/sub-01/eeg/sub-01.xyz).
[14]:
# Let's have a look at the EEG data
with warnings.catch_warnings(): # suppress some irrelevant warnings coming from mne.read_epochs_eeglab()
warnings.simplefilter("ignore")
epochs_eeglab = mne.read_epochs_eeglab(
os.path.join(output_dir, __eeglab_directory__,
participant_label, 'eeg',
participant_label+'_task-faces_desc-preproc_eeg.set')
) # sub-01_FACES_250HZ_prepd.set
# eeglab2fif removes a baseline and crops the epochs according to parameters start_t and end_t in config file
start_t = -0.2
end_t = 0.6
epochs_eeglab.apply_baseline((start_t,0))
epochs_eeglab.crop(tmin=start_t,tmax=end_t)
evoked_eeglab = epochs_eeglab.average().pick('eeg')
# compare to what eeglab2fif saved
epochs_mne = mne.read_epochs(
os.path.join(output_dir, __cmp_directory__,
participant_label, 'eeg',
participant_label+'_epo.fif'))
evoked_mne = epochs_mne.average().pick('eeg')
Extracting parameters from /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/eeglab-v14.1.1/sub-01/eeg/sub-01_task-faces_desc-preproc_eeg.set...
Not setting metadata
Not setting metadata
588 matching events found
No baseline correction applied
0 projection items activated
Ready.
Applying baseline correction (mode: mean)
Reading /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_epo.fif ...
Read a total of 1 projection items:
Average EEG reference (1 x 128) idle
Found the data of interest:
t = -200.00 ... 600.00 ms
0 CTF compensation matrices available
Not setting metadata
Not setting metadata
588 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 1)
1 projection items activated
[15]:
# plot and convince yourself it's the same
%matplotlib inline
fig = plt.figure()
plt.rcParams['figure.figsize'] = (15, 10)
_=evoked_mne.plot(time_unit='s')
fig = plt.figure()
plt.rcParams['figure.figsize'] = (15, 10)
_=evoked_eeglab.plot(time_unit='s')
<Figure size 432x288 with 0 Axes>
<Figure size 1080x720 with 0 Axes>
createsrc
MNE is able to create volume- and surface-based source spaces, but in our pipeline, we use surface-based only. In order to do this, MNE takes advantage of the Freesurfer-created outputs in the derivatives/freesurfer-7.1.1 derivatives directory.
createbem
The BEM (boundary element model) is the head model we use, in our case, it is based on the individual’s structural MRI and, again, related freesurfer derivatives. Its creation consists of two steps:
The necessary surfaces (brain, inner skull, outer skull, and outer skin) are extracted using
mne.bem.make_watershed_bem(). The surfaces are saved in the subject’s freesurfer-directory in a new folderbem/watershed.The model itself is created using
mne.make_bem_model()andmne.make_bem_solution(). In this step, the surfaces and the tissue conductivities between the surfaces are used.
[16]:
# Let's visualize the BEM surfaces and source space
src = mne.read_source_spaces(os.path.join(output_dir,__cmp_directory__,\
participant_label,'eeg',participant_label+'_src.fif'))
# plot will appear in separate window
%matplotlib qt
# lines are the surfaces, pink dots are the sources (dipoles)
_=mne.viz.plot_bem(subject=participant_label, subjects_dir=project.freesurfer_subjects_dir,
brain_surfaces='white', src=src, orientation='sagittal')
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
Using surface: /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/bem/inner_skull.surf
Using surface: /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/bem/outer_skull.surf
Using surface: /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/freesurfer-7.1.1/sub-01/bem/outer_skin.surf
Loader stage
During the preparer stage, we have told CMP3 which file extensions and keywords to look for that are going to be used in the actual inverse solution. Using nipype’s “BIDS datagrabber”, it gathers the necessary inputs in this step.
Inverse solution stage
The only implemented inverse solution algorithm right now is sLORETA.
This stage has again three processing steps:
createfwd: creates the forward solution (leadfield) from the BEM and the source spacecreatecov: creates the noise covariance matrix from the datainvsol_MNE: creates the actual inverse operator and applies it, resulting in ROI-time courses
createfwd
MNE first computes a forward solution that describes how electrical currents propagate from the sources created earlier (via createsrc) through the tissues of the head modelled by the BEM (created via createbem) to the electrodes. Thus, the electrode positions have to be known and be aligned to the head model.
[17]:
# Let's check the alignment between MRI and electrode positions.
trans = mne.read_trans(os.path.join(output_dir,__cmp_directory__,\
participant_label,'eeg',participant_label+'_trans.fif'))
mne.viz.plot_alignment(epochs_mne.info, trans=trans, subject=participant_label,
subjects_dir=project.freesurfer_subjects_dir, dig=False,
surfaces=dict(head=0.95), coord_frame='mri')
Using mayavi 3d backend.
Using outer_skin.surf for head surface.
Channel types:: eeg: 128
2022-02-21 11:12:43.725 ( 3.624s) [ 23F39700]vtkInteractorStyleTrack:27 WARN| vtkInteractorStyleTrackball (0x55ed299ef300): vtkInteractorStyleTrackball will be deprecated in
the next release after VTK 4.0. Please use
vtkInteractorStyleSwitch instead.
[17]:
<mayavi.core.scene.Scene at 0x7f972ef24290>
createcov
MNE uses an estimate of signal to noise ratio in its creation of the inverse solution. For that, it considers the pre-stimulus period of the EEG recordings.
[18]:
# Let's have a look at the noise covariance.
%matplotlib inline
noise_cov = mne.read_cov(os.path.join(output_dir,__cmp_directory__,\
participant_label,'eeg',participant_label+'_noisecov.fif'))
fig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, epochs_mne.info)
128 x 128 full covariance (kind = 1) found.
Read a total of 1 projection items:
Average EEG reference (1 x 128) active
Computing rank from covariance with rank=None
Using tolerance 1.2e-14 (2.2e-16 eps * 128 dim * 0.43 max singular value)
Estimated rank (eeg): 127
EEG: rank 127 computed from 128 data channels with 0 projectors
invsol_MNE
Now, everything comes together to create the inverse operator, which is then applied to the EEG data to create source time courses. In the last step, the source time courses are converted to ROI-time courses according to the selected parcellation.
The outputs that are necessary for this step to work were created in the previous processing steps, namely:
the EEG epochs in .fif-format
the electrode montage
the head model
the source point locations
the forward operator
the noise covariance
First, the inverse operator is created using mne.minimum_norm.make_inverse_operator(). We use the options
loose=1, depth=None, fixed=False
This means that we are obtaining full 3-dimensional dipoles whose orientation is not fixed or constrained to be (somewhat) orthogonal to surface; and we are not applying any depth weighting. The solution is written to a file sub-01_inv.fif in the same directory as the other outputs (derivatives/cmp-v3.0.3/sub-01/eeg).
In a subsequent step in the same interface, this inverse operator is then applied to the epochs (not the evoked time course averaged over trials) using mne.minimum_norm.apply_inverse_epochs.
The final step performed by this interface and by the EEG pipeline is to use mne.extract_label_time_course to create ROI-time courses according to mne.read_labels_from_annot(). As given in the config file, we use “lausanne2008” scale 1, which is the Desikan-atlas. The time courses and the ROI-names are stored in sub-01_atlas-L2018_res-scale1_desc-epo_timeseries.pkl, i.e. a format that is not specific to MNE.
Let’s have a look at the time courses.
[19]:
with open(os.path.join(output_dir,__cmp_directory__,participant_label,'eeg',participant_label + '_atlas-L2018_res-scale1_desc-epo_timeseries.pkl'), 'rb') as f:
rtc_epo = pickle.load(f)
# for some reason, MNE writes label time courses as lists. convert to numpy array
rtc_epo['data'] = np.array(rtc_epo['data'])
[20]:
# sort labels to make the time courses look nicer
N = len(rtc_epo['labels'])-2 # two "unknown" regions - do not plot
sorting = list(np.arange(0,N,2))+list(np.arange(1,N,2)) # left and right always alternating
# list of ROI names
labels_list_left = [i.name for i in rtc_epo['labels'][0::2] if i.name!='unknown -lh']
labels_list_right = [i.name for i in rtc_epo['labels'][1::2] if i.name!='unknown -rh']
labels_list = labels_list_left+labels_list_right
[21]:
# plot
%matplotlib inline
to_plot = np.mean(rtc_epo['data'][:,:-2,:],axis=0)
vminmax = np.max(abs(to_plot))
plt.rcParams['figure.figsize'] = (15, 10)
plt.imshow(to_plot[sorting,:],aspect='auto',extent=[-200,600,0,67],interpolation='None',vmin=-vminmax,vmax=vminmax,cmap='PiYG');
plt.xlabel('ms')
plt.ylabel('ROIs')
cbar = plt.colorbar()
cbar.set_label('source activity (a.u.)')
locs = np.arange(0,N)
_=plt.yticks(locs,labels_list[-1::-1] )
We can see that some of the time courses are “flipped” (have the opposite sign of the others). We will not address this problem here, but this is because of the step where dipole time courses are summarized for each brain region, using PCA. The direction of the resulting vector is not uniquely defined.
Connectivity measures
Of course, the idea of CMP3 is to provide connectomes! This interface is not implemented yet in the pipeline, but with the ROI-time courses, it is easy to obtain functional connectivity matrices using MNE-Connectivity.
Keep in mind that we only plot a single subject’s connectivity here, so it is not surprising if you do not see exactly what you would expect.
[22]:
%%time
sfreq = epochs_mne.info['sfreq'] # the sampling frequency
con_methods = ['pli', 'wpli2_debiased', 'ciplv']
label_ts = rtc_epo['data']
con = mnec.spectral_connectivity(
label_ts, method=con_methods, mode='multitaper', sfreq=sfreq,
faverage=True, mt_adaptive=True, n_jobs=1,
verbose='WARNING')
# Prepare the connectivity data for saving with CMP3 the connectome files
# con is a 3D array, get the connectivity for the first (and only) freq. band
# for each method
con_res = dict()
for method, c in zip(con_methods, con):
con_res[method] = np.squeeze(c.get_data(output='dense'))
CPU times: user 54.6 s, sys: 0 ns, total: 54.6 s
Wall time: 54.7 s
You can now save the results with the cmtklib.eeg.save_eeg_connectome_file() function provided by CMP3, in the same formats (['tsv', 'gpickle', 'mat', 'graphml']) as the diffusion MRI and resting-state fMRI pipelines.
[23]:
from cmtklib.eeg import save_eeg_connectome_file
[24]:
conn_output = os.path.join(
bids_dir, 'derivatives', 'cmp-v3.0.3', participant_label, 'eeg'
)
conn_basename = f'{participant_label}_atlas-L2018_res-scale1_conndata-network_connectivity'
save_eeg_connectome_file(
con_res=con_res,
roi_labels=[label.name for label in rtc_epo['labels']], # Get parcellationlabels used by MNE
output_dir=conn_output,
output_basename=conn_basename,
output_types=['tsv', 'gpickle', 'mat', 'graphml']
)
Save /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_atlas-L2018_res-scale1_conndata-network_connectivity.tsv...
Save /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_atlas-L2018_res-scale1_conndata-network_connectivity.gpickle...
Save /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_atlas-L2018_res-scale1_conndata-network_connectivity.mat...
Save /home/localadmin/Softwares/GitHub/connectomemapper3/docs/notebooks/ds003505_demo/derivatives/cmp-v3.0.3/sub-01/eeg/sub-01_atlas-L2018_res-scale1_conndata-network_connectivity.graphml...
We can load the matrices in network format, by reading the gpickle files using Networkx:
[25]:
G = nx.read_gpickle(os.path.join(conn_output, f"{conn_basename}.gpickle")) # same format with tsv
A_wpli2_debiased = nx.to_numpy_array(G, weight="wpli2_debiased")
A_wpli2_debiased
[25]:
array([[ 0.00000000e+00, -3.66497642e-04, -4.44626249e-04, ...,
2.61330892e-04, -6.55801234e-04, -7.51796246e-04],
[-3.66497642e-04, 0.00000000e+00, 1.73599618e-03, ...,
1.88210359e-03, 2.06624042e-03, 1.58885857e-03],
[-4.44626249e-04, 1.73599618e-03, 0.00000000e+00, ...,
1.20817345e-03, -1.18799242e-03, -1.27701877e-03],
...,
[ 2.61330892e-04, 1.88210359e-03, 1.20817345e-03, ...,
0.00000000e+00, 2.14315916e-03, -8.06204062e-05],
[-6.55801234e-04, 2.06624042e-03, -1.18799242e-03, ...,
2.14315916e-03, 0.00000000e+00, 1.98345454e-03],
[-7.51796246e-04, 1.58885857e-03, -1.27701877e-03, ...,
-8.06204062e-05, 1.98345454e-03, 0.00000000e+00]])
You can then visualize the connectivity matrix in a pretty circular layout with MNE-Connectivity viz.plot_connectivity_circle():
[26]:
%%time
label_names = [label.name for label in rtc_epo['labels']]
lh_labels = [name for name in label_names if name.endswith('lh')]
# Get the y-location of the label
label_ypos = list()
for name in lh_labels:
idx = label_names.index(name)
ypos = np.mean(rtc_epo['labels'][idx].pos[:, 1])
label_ypos.append(ypos)
# Reorder the labels based on their location
lh_labels = [label for (yp, label) in sorted(zip(label_ypos, lh_labels))]
# For the right hemi
rh_labels = [label[:-2] + 'rh' for label in lh_labels]
# Save the plot order and create a circular layout
node_order = list()
node_order.extend(lh_labels[::-1]) # reverse the order
node_order.extend(rh_labels)
node_angles = mnec.viz.circular_layout(label_names, node_order, start_pos=90,
group_boundaries=[0, len(label_names) / 2])
# Plot the graph using node colors from the FreeSurfer parcellation. We only
# show the 300 strongest connections.
# plot will appear in separate window
%matplotlib inline
mnec.viz.plot_connectivity_circle(A_wpli2_debiased, label_names, n_lines=300,
node_angles=node_angles, node_colors='r',
title='')
CPU times: user 14.1 s, sys: 182 ms, total: 14.2 s
Wall time: 14 s
[26]:
(<Figure size 576x576 with 2 Axes>, <PolarAxesSubplot:>)
This concludes the tutorial 🧠!
We hope you enjoy it and any feedback or suggestions to improve it are very welcome! Just please open a new issue on GitHub and share your thoughts with us.
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