https://github.com/openmeeg/openmeeg_sample_data
OpenMEEG demo dataset with shell / bat / python scripts
https://github.com/openmeeg/openmeeg_sample_data
Last synced: about 1 month ago
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OpenMEEG demo dataset with shell / bat / python scripts
- Host: GitHub
- URL: https://github.com/openmeeg/openmeeg_sample_data
- Owner: openmeeg
- Created: 2012-02-20T10:15:20.000Z (over 13 years ago)
- Default Branch: master
- Last Pushed: 2022-08-24T08:37:06.000Z (over 2 years ago)
- Last Synced: 2024-03-26T07:15:42.502Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 2.05 MB
- Stars: 3
- Watchers: 8
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.rst
Awesome Lists containing this project
README
.. -*- mode: rst -*-
|GitHub Actions|_
.. |GitHub Actions| image:: https://github.com/openmeeg/openmeeg_sample_data/actions/workflows/testing.yml/badge.svg
.. _Github Actions: https://github.com/openmeeg/openmeeg_sample_data/actions/workflows/testing.yml
CAVEAT on using the python wrapper (see bottom)
**These examples contain mainly two different views on how to use OpenMEEG with scripting languages**
I) Computing/viewing EEG/MEG/EIT leadfields from bash, DOS, or Python
---------------------------------------------------------------------
It concerns files compute_leadfields.* view_leadfields.* mesh.py example_inverse_problem.py view_head_model.py.
The viewer works with mayavi.
II) Compute and apply a corticial mapping, TDCS (Transcranial direct-current stimulation)
-----------------------------------------------------------------------------------------
It concerns files::
corticalmapping*.py tdcs.py data/canonical/* data/canonical_real/*
and uses Python tools for viewing::
om_display.py, om_compare.py, om_basics.py
The viewer works with VTK.
Here are more details:
I) Computing/viewing EEG/MEG/EIT leadfields
-------------------------------------------
Demo scripts to compute leadfields with OpenMEEG:
- Supports EEG, MEG, EIT and Internal potential leadfields
This folder contains a sample realistic dataset for EEG, MEG, EIT
and internal potential forward modeling.
The head model is a 3 layers model with 3 nested meshes::
brain.tri, skull.tri and head.tri
To run the computation you can use the scripts:
On windows (bat script):
------------------------
compute_leadfields.bat
On Linux or Mac OS X (bash script):
-----------------------------------
./compute_leadfields.sh
Or using Python:
----------------
python compute_leadfields.py
The leadfields computed are stored in (matlab format):
eeg_leadfield.mat (for EEG)
meg_leadfield.mat (for MEG)
eit_leadfield.mat (for EIT)
ip_leadfield.mat (for Internal Potential)
The files used during the BEM computation are stored in the "tmp" folder.
See sample_output.txt to see what the scripts output should look like.
On a recent workstation the computation takes about::
744.66 s 344.12 s 390.41 s om_assemble -HM
+ 431.74 s 188.5 s 246.5 s om_inverser
+ 2689.71 s 545.1 s 719.13 s om_assemble -DSM
+ 0.13 s 0.02 s 0.04 s om_assemble -H2EM
+ 80.92 s 3.19 s 16.42 s om_gain -EEG
+ 3.39 s. 0.88 s 1.23 s om_assemble -H2MM
+ 2.33 s 0.21 s 0.95 s om_assemble -DS2MM
+ 84.84 s 4.17 s 17.65 s om_gain -MEG
173.49 s om_assemble -EITSM
1.15 s om_gain -EEG
II) Compute and apply a corticial mapping, TDCS
-----------------------------------------------
- *canonical* is a model generated remeshing the MNI template from SPM. It uses conductivities, 1., 0.0125, 1. and has a few defined source terms (8) with intensities roughly at the same scale as the model. 128 electrodes.
- *canonical_real* is the same model with real potentials and sensors (64) locations.
commands:
---------
For cortical mapping:
$ python corticalmapping.py canonical_real
For TDCS:
$ python tdcs.py canonical
!!! CAVEAT on using OpenMEEG from Python !!!
=============================================
Beware that a temporary object has its memory released. So do not work with data provided from an OpenMEEG temporary object.
For example, having a symmetric matrix defined as:
>>> M = om.SymMatrix(100)
Taking as a numpy array the sub-matrix of M might lead to corrupted memory:
>>> mySubMat = om.asarray(M.submat(0,10,0,10))
since submat returns a newly created object that is hold by nothing, thus destroyed afterward.
Instead do keep an object pointing the newly created submatrix, and then
access the numpy array form of it:
>>> subM = M.submat(0,10,0,10)
>>> mySubMat = om.asarray(subM)
If you meet some difficulties running this example please contact:
The OpenMEEG developers.