MNE-ICALabel

MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python - Published in JOSS (2022)

https://github.com/mne-tools/mne-icalabel

Science Score: 98.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 9 DOI reference(s) in README and JOSS metadata
✓
Academic publication links
Links to: joss.theoj.org
○
Committers with academic emails
○
Institutional organization owner
✓
JOSS paper metadata
Published in Journal of Open Source Software

Keywords

eeg ica neuroscience python

Keywords from Contributors

electroencephalography magnetoencephalography meg neuroimaging bids ecog electrocorticography ieeg mne meshing

Scientific Fields

Medicine Life Sciences - 84% confidence

Mathematics Computer Science - 84% confidence

Last synced: 4 months ago · JSON representation ·

Repository

Automatic labeling of ICA components in Python.

Basic Info

Host: GitHub
Owner: mne-tools
License: bsd-3-clause
Language: Python
Default Branch: main
Homepage: https://mne.tools/mne-icalabel/dev/index.html
Size: 175 MB

Statistics

Stars: 104
Watchers: 8
Forks: 18
Open Issues: 14
Releases: 9

Topics

eeg ica neuroscience python

Created over 4 years ago · Last pushed 4 months ago

Metadata Files

Readme Contributing License Citation

mne-icalabel

This repository is a conversion of the popular Matlab-based ICLabel classifier for Python. In addition, mne-icalabel provides extensions and improvements in the form of other models.

Why?

EEG and MEG recordings include artifacts, such as heartbeat, eyeblink, muscle, and movement activity. Independent component analysis (ICA) is a common method to remove artifacts, but typically relies on manual annotations labelling which independent components (IC) reflect noise and which reflect brain activity.

This package aims at automating this process, using the popular MNE-Python API for EEG, MEG and iEEG data.

Basic Usage

MNE-ICALabel estimates the labels of ICA components given a MNE-Python Raw or Epochs object and an ICA instance using the ICA decomposition available in MNE-Python.

``` from mneicalabel import labelcomponents

assuming you have a Raw and ICA instance previously fitted

label_components(raw, ica, method='iclabel') ```

The only current available method is 'iclabel'.

Documentation

Stable version documentation. Dev version documentation.

Installation

The current stable release of mne-icalabel can be installed with pip, for example, by running:

pip install mne-icalabel

For further details about installation, see the install page.

To get the latest (development) version, using git, open a terminal and type:

git clone git://github.com/mne-tools/mne-icalabel.git
cd mne-icalabel
pip install -e .

The development version can also be installed directly using pip:

pip install https://api.github.com/repos/mne-tools/mne-icalabel/zipball/main

Alternatively, you can also download a zip file of the latest development version.

Contributing

If you are interested in contributing, please read the contributing guidelines.

Getting Help

For any usage questions, please post to the MNE Forum. Be sure to add the mne-icalabel tag to your question.

Citing

If you use the mne-icalabel, please consider citing our paper:

@article{Li2022, title = {MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python}, volume = {7}, ISSN = {2475-9066}, url = {http://dx.doi.org/10.21105/joss.04484}, DOI = {10.21105/joss.04484}, number = {76}, journal = {Journal of Open Source Software}, publisher = {The Open Journal}, author = {Li, Adam and Feitelberg, Jacob and Saini, Anand Prakash and H\"{o}chenberger, Richard and Scheltienne, Mathieu}, year = {2022}, month = aug, pages = {4484} }

And the paper associated to the model used:

ICLabel

@article{PionTonachini2019, title = {ICLabel: An automated electroencephalographic independent component classifier, dataset, and website}, volume = {198}, ISSN = {1053-8119}, url = {http://dx.doi.org/10.1016/j.neuroimage.2019.05.026}, DOI = {10.1016/j.neuroimage.2019.05.026}, journal = {NeuroImage}, publisher = {Elsevier BV}, author = {Pion-Tonachini, Luca and Kreutz-Delgado, Ken and Makeig, Scott}, year = {2019}, month = sep, pages = {181–197} }

Future versions of the software are aimed at improved models and may have different papers associated with it.

Owner

Name: MNE tools for MEG and EEG data analysis
Login: mne-tools
Kind: organization

Website: http://mne.tools
Repositories: 45
Profile: https://github.com/mne-tools

JOSS Publication

MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python

Published

August 26, 2022

DOI

10.21105/joss.04484

Volume 7, Issue 76, Page 4484

Authors

Adam Li

Department of Computer Science, Columbia University, New York, United States

Jacob Feitelberg

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States

Anand Prakash Saini

Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland

Richard Höchenberger

Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Germany

Mathieu Scheltienne

Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland, Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland, Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland

Editor

Elizabeth DuPre

Citation (CITATION.cff)

# YAML 1.2
---
# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/)
cff-version: 1.2.0
title: 'MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python'
abstract: 'MNE-ICALabel is a Python library to facilitate the automatic annotation of ICA components for MEG, EEG, and iEEG data with MNE-Python. More information about MNE-Python can be found at <a href="https://mne.tools">mne.tools</a>.'
authors:
    - given-names: Adam
      family-names: Li
      affiliation: 'Department of Computer Science, Columbia University, New York, NY, USA'
      orcid: 'https://orcid.org/0000-0001-8421-365X'
    - given-names: Richard
      family-names: Höchenberger
      affiliation: 'Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Germany'
      orcid: 'https://orcid.org/0000-0002-0380-4798'
    - given-names: Thomas
      family-names: Donoghue
      affiliation: 'Department of Biomedical Engineering, Columbia University'
      orcid: 'https://orcid.org/0000-0001-5911-0472'
    - given-names: Mathieu
      family-names: Scheltienne
      affiliation: 'Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland'
      orcid: 'https://orcid.org/0000-0001-8316-7436'
    - given-names: Jacob
      family-names: Feitelberg
      affiliation: 'Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA'
      orcid: 'https://orcid.org/0000-0002-4551-0245'
    - given-names: Anand
      family-names: Saini
      affiliation: 'Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland'
      orcid: 'https://orcid.org/0000-0003-3595-5969'
type: software
repository-code: 'https://github.com/mne-tools/mne-icalabel'
license: BSD-3-Clause
keywords:
  - neuroscience
  - neuroimaging
  - mne
  - python
  - magnetoencephalography
  - meg
  - electroencephalography
  - eeg
  - ieeg
  - ica
message: >-
  Please cite this software using the metadata from
  'preferred-citation' in the CITATION.cff file.
preferred-citation:
  title: >-
    MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python
  journal: Journal of Open Source Software
  type: article
  year: 2022
  volume: 7
  issue: 76
  start: 4484
  doi: 10.21105/joss.04484
  authors:
    - given-names: Adam
      family-names: Li
      affiliation: 'Department of Computer Science, Columbia University, New York, NY, USA'
      orcid: 'https://orcid.org/0000-0001-8421-365X'
    - given-names: Richard
      family-names: Höchenberger
      affiliation: 'Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Germany'
      orcid: 'https://orcid.org/0000-0002-0380-4798'
    - given-names: Thomas
      family-names: Donoghue
      affiliation: 'Department of Biomedical Engineering, Columbia University'
      orcid: 'https://orcid.org/0000-0001-5911-0472'
    - given-names: Mathieu
      family-names: Scheltienne
      affiliation: 'Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland'
      orcid: 'https://orcid.org/0000-0001-8316-7436'
    - given-names: Jacob
      family-names: Feitelberg
      affiliation: 'Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA'
      orcid: 'https://orcid.org/0000-0002-4551-0245'
    - given-names: Anand
      family-names: Saini
      affiliation: 'Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland'
      orcid: 'https://orcid.org/0000-0003-3595-5969'
identifiers:
  - description: "Code archive on Zenodo"
    type: doi
    value: 10.5281/zenodo.7017165
...

GitHub Events

Total

Issues event: 4
Watch event: 14
Delete event: 43
Issue comment event: 21
Push event: 51
Pull request review comment event: 27
Pull request review event: 26
Pull request event: 82
Fork event: 3
Create event: 39

Last Year

Issues event: 4
Watch event: 14
Delete event: 43
Issue comment event: 21
Push event: 51
Pull request review comment event: 27
Pull request review event: 26
Pull request event: 82
Fork event: 3
Create event: 39

Committers

Last synced: 5 months ago

All Time

Total Commits: 323
Total Committers: 11
Avg Commits per committer: 29.364
Development Distribution Score (DDS): 0.749

Past Year

Commits: 57
Committers: 5
Avg Commits per committer: 11.4
Development Distribution Score (DDS): 0.211

Top Committers

Name	Email	Commits
Adam Li	a**2@g**m	81
pre-commit-ci[bot]	6****]	74
Mathieu Scheltienne	m**e@f**h	64
Jacob Feitelberg	j**g@g**m	45
Mathieu Scheltienne	m**e@g**m	29
dependabot[bot]	4****]	11
Anand Saini	3****4	7
github-actions[bot]	4****]	7
Tom	t**h@g**m	2
Anand Saini	a**i@f**h	2
Scott Huberty	5****y	1

Committer Domains (Top 20 + Academic)

fcbg.ch: 2

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 57
Total pull requests: 228
Average time to close issues: about 2 months
Average time to close pull requests: 10 days
Total issue authors: 21
Total pull request authors: 9
Average comments per issue: 4.21
Average comments per pull request: 1.26
Merged pull requests: 208
Bot issues: 0
Bot pull requests: 142

Past Year

Issues: 3
Pull requests: 84
Average time to close issues: 1 day
Average time to close pull requests: about 5 hours
Issue authors: 3
Pull request authors: 5
Average comments per issue: 2.67
Average comments per pull request: 0.19
Merged pull requests: 76
Bot issues: 0
Bot pull requests: 82

View more stats

Top Authors

Issue Authors

adam2392 (13)
hoechenberger (11)
mscheltienne (8)
adswa (5)
scott-huberty (2)
CarinaFo (2)
TomDonoghue (2)
chmendoza (1)
FedeC3N (1)
sjyzhu (1)
alexrockhill (1)
Ayumu722 (1)
vferat (1)
lucapton (1)
visserle (1)

Pull Request Authors

pre-commit-ci[bot] (137)
mscheltienne (57)
adam2392 (22)
github-actions[bot] (11)
dependabot[bot] (10)
anandsaini024 (4)
TomDonoghue (3)
colehank (2)
jacobf18 (1)
scott-huberty (1)

Top Labels

Issue Labels

🌟 enhancement (1) help wanted (1)

Pull Request Labels

dependencies (6) 🐞 bug (4) 📓 doc (1) 🌟 enhancement (1)

Packages

Total packages: 2
Total downloads:
- pypi 4,012 last-month
Total docker downloads: 12

Total dependent packages: 3
(may contain duplicates)
Total dependent repositories: 3
(may contain duplicates)
Total versions: 14
Total maintainers: 2

pypi.org: mne-icalabel

MNE-ICALabel: Automatic labeling of ICA components from MEG, EEG and iEEG data with MNE.

Documentation: https://mne-icalabel.readthedocs.io/
License: BSD 3-Clause License Copyright (c) 2022, MNE All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Latest release: 0.7.0
published over 1 year ago

Versions: 9
Dependent Packages: 3
Dependent Repositories: 3
Downloads: 4,012 Last month
Docker Downloads: 12

Rankings

Dependent packages count: 2.4%

Docker downloads count: 3.9%

Average: 5.2%

Downloads: 5.5%

Dependent repos count: 9.0%

Maintainers (2)

adam2392 mscheltienne

Last synced: 4 months ago

conda-forge.org: mne-icalabel

Homepage: https://mne.tools/mne-icalabel/stable/
License: BSD-3-Clause
Latest release: 0.3.1
published over 3 years ago

Versions: 5
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent repos count: 34.0%

Stargazers count: 38.2%

Average: 43.2%

Forks count: 49.6%

Dependent packages count: 51.2%

Last synced: 4 months ago

Dependencies

.github/workflows/draft-pdf.yml actions

actions/checkout v3 composite
actions/upload-artifact v3 composite
openjournals/openjournals-draft-action master composite

pyproject.toml pypi

importlib-metadata python_version<"3.8"
importlib-resources python_version<"3.9"
mne >= 1.1
numpy >= 1.21
pooch *
scipy >= 1.2.0
torch *

.github/workflows/doc.yaml actions

JamesIves/github-pages-deploy-action v4 composite
actions/checkout v4 composite
actions/download-artifact v3 composite
actions/setup-python v4 composite
actions/upload-artifact v3 composite
pyvista/setup-headless-display-action main composite

.github/workflows/publish.yaml actions

actions/checkout v4 composite
actions/setup-python v4 composite

.github/workflows/pytest.yaml actions

actions/checkout v4 composite
actions/setup-python v4 composite
codecov/codecov-action v3 composite
pyvista/setup-headless-display-action main composite

doc/_static/versions.json meteor

.github/workflows/stubs.yaml actions

actions/checkout v4 composite
actions/setup-python v5 composite
pyvista/setup-headless-display-action main composite

MNE-ICALabel

Science Score: 98.0%

Keywords

Keywords from Contributors

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

mne-icalabel

Why?

Basic Usage

assuming you have a Raw and ICA instance previously fitted

Documentation

Installation

Contributing

Getting Help

Citing

Owner

JOSS Publication

MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python

Authors

Editor

Tags

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: mne-icalabel

Rankings

Maintainers (2)

conda-forge.org: mne-icalabel

Rankings

Dependencies