Package: mne-pipeline-hd
mne-pipeline-hdA Pipeline-GUI forMNE-Pythonfrom MEG-Lab HeidelbergInstallationInstall MNE-python as instructed on
thewebsite,
I would recommend to install in a separate conda environment with:conda env create -n mne_p -f environment.ymlconda activate mne_ppip install https://github.com/marsipu/mne-pipeline-hd/zipball/mainUpdateRunpip install --upgrade --no-deps --force-reinstall https://github.com/marsipu/mne-pipeline-hd/zipball/mainfor an update to the development version
orpip install --upgrade mne-pipeline-hdfor the latest release.StartRunmne_pipeline_hdin your mne_pipeline-environment (conda activate mne_p)orrun __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-RequestPlease report bugs on GitHub as an issue or to me (dev@earthman-music.de)
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 (dev@earthman-music.de)Contribute and build your own functions/fix bugsI you want to help by contributing, I would be very happy:You need aGitHub-Accountand should
havegitinstalled.Fork this repository on GitHubMove to the folder where you want to clone toCloneyour forked repositorywith git from a
terminal:git clone <url you get from the green clone-button from your forked repository on GitHub>Add upstream to git for
updates:git remote add upstream git://github.com/marsipu/mne-pipeline-hd.gitInstall development version with pip:pip install -e ./Create a branch for changes:git checkout -b <branch-name>Commit changes:git commit -am "<your commit message>"Push changes to your forked repository on GitHub:git pushMake "New pull request" from your new feature branchYou can alwayswrite me, if you have questions
about the contribution-process
or about the program-structure.AcknowledgmentsThis Pipeline is build on top
ofMNE-PythonA. 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,DOIIt was inspired by a pipeline
fromLau M. AndersenAndersen 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
integratesautorejectMainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre
Gramfort. 2017.
“Autoreject: Automated artifact rejection for MEG and EEG data”. NeuroImage,
159, 417-429.Many ideas and basics for GUI-Programming where taken
fromLearnPyQtand numerous
stackoverflow-questions/solutions.The development is financially supported
byHeidelberg University.Thank you to the members of my laboratory (especially my
supervisorAndre Rupp)
for their feedback and testing in the early stages of development.