mne-pipeline-hd 0.3.5

A Pipeline-GUI for MNE-Python from MEG-Lab Heidelberg

mne-pipeline-hd

A Pipeline-GUI for MNE-Python from MEG-Lab Heidelberg

Installation

1. Install MNE-python as instructed on the website
2. To install mne_pipeline_hd in the conda-enviroment you created in step 1 you can either
   • Install the stable version with pip install mne_pipeline_hd
   • Install the development version with pip install git+https://github.com/marsipu/mne_pipeline_hd.git@main

Update

Run pip install --upgrade --no-deps --force-reinstall git+https://github.com/marsipu/mne_pipeline_hd.git@main for an update to the development version or pip install --upgrade mne-pipeline-hd for the latest stable release.

Start

Run mne_pipeline_hd in your conda-environment where you installed mne-python and mne-pipeline-hd.

or

run __main__.py from the terminal or an IDE like PyCharm, VSCode, Atom, etc.

When using the pipeline and its functions bear in mind that the pipeline is still in development! The basic functions supplied are just a suggestion and you should verify before usage if they do what you need. They are also partly still adjusted to specific requirements which may not apply to all data.

Bug-Report/Feature-Request

Please report bugs on GitHub as an issue or to me ([e-mail]) directly. And if you got ideas on how to improve the pipeline or some feature-requests, you are welcome to open an issue too or send an e-mail ([e-mail])

Contribute and build your own functions/fix bugs

I you want to help by contributing, I would be very happy:

You need a GitHub-Account and should have git installed.

1. Fork this repository on GitHub
2. Move to the folder where you want to clone to
3. Clone your forked repository with git from a terminal: git clone <url you get from the green clone-button from your forked repository on GitHub>
4. Add upstream to git for updates: git remote add upstream git://github.com/marsipu/mne-pipeline-hd.git
5. Install development version with pip: pip install -e .[tests]
6. Install the pre-commit hooks with: pre-commit install
7. Create a branch for changes: git checkout -b <branch-name>
8. Commit changes: git commit -am "<your commit message>"
9. Push changes to your forked repository on GitHub: git push
10. Make "New pull request" from your new feature branch

You can always write me an e-mail, if you have questions about the contribution-process or about the program-structure.

Acknowledgments

This Pipeline is build on top of MNE-Python

A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, MNE software for processing MEG and EEG data, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119, DOI

It was originally inspired by a pipeline from Lau M. Andersen

Andersen LM. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front Neurosci. 2018 Jan 22;12:6. doi: 10.3389/fnins.2018.00006. PMID: 29403349; PMCID: PMC5786561.

This program also integrates autoreject

Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. 2017. “Autoreject: Automated artifact rejection for MEG and EEG data”. NeuroImage, 159, 417-429.

The colorpalettes for light and dark theme are inspired from PyQtDarkTheme.

Many ideas and basics for GUI-Programming where taken from LearnPyQt and numerous stackoverflow-questions/solutions.

The development is financially supported by Heidelberg University.

Thank you to the members of my laboratory (especially my supervisor Andre Rupp) for their feedback and testing in the early stages of development.