laMEG laMEG: laminar inference with MEG#

Introduction#

Toolbox for laminar inference with MEG, powered by FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki) and SPM (spm/) for Python 3.7.

The source code of the project is hosted on Github at the following address: danclab/laMEG

To get started, follow the installation instructions.

Available modules#

Here is a list of the modules available in laMEG:

invert

This module provides tools for the coregistration, and source reconstruction of MEG data using SPM (Statistical Parametric Mapping).

laminar

This module provides tools for performing laminar analysis of MEG signals.

surf

This module provides a set of tools for handling and manipulating surface mesh data.

viz

This module provides tools for converting and visualizing data.

simulate

This module facilitates the simulation of MEG data using the Statistical Parametric Mapping (SPM) toolbox.

Tutorials#

A collection of tutorials is available:

Operating system#

  • Windows: Tested on WSL (using Ubuntu 24.04.1), follow instructions here

  • Mac: May work, not tested

  • Linux: Tested on Ubuntu and Debian

Requirements#

Installation#

  1. Create a conda environment:

    conda create -n <env name> python=3.7

    replacing <env name> with the name of the environment you would like to create (i.e. ‘lameg’, or the name of your project)

  2. Activate the environment:

    conda activate <env name>

    replacing <env name> with name of the environment you created.

  3. Install FreeSurfer, following the instructions here

  4. To install laMEG, run:

    pip install lameg

    This also installs SPM standalone and Matlab runtime, which can take some time depending on your connection speed.

  5. Before using, deactivate and reactivate the environment for changes to environment variables to take effect:

    conda deactivate
conda activate <env name>

  6. If you want to run the tutorials, download and extract the test data

Indices and tables#

Funding#

Supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme grant agreement 864550, and a seed grant from the Fondation pour l’Audition.

ERC FPA