| Name | mne-icalabel JSON |
| Version |
0.8.1
JSON |
| download |
| home_page | None |
| Summary | MNE-ICALabel: Automatic labeling of ICA components from MEG, EEG and iEEG data with MNE. |
| upload_time | 2025-10-17 17:43:01 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| 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.
|
| keywords |
eeg
ica
ieeg
meg
neuroscience
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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|
| coveralls test coverage |
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|
# mne-icalabel
[](https://github.com/astral-sh/ruff)
[](https://github.com/psf/black)
[](https://pycqa.github.io/isort/)
[](https://codecov.io/gh/mne-tools/mne-icalabel)
[](https://github.com/mne-tools/mne-icalabel/actions/workflows/pytest.yaml)
[](https://github.com/mne-tools/mne-icalabel/actions/workflows/doc.yaml)
[](https://pypi.org/project/mne-icalabel/)
[](https://pepy.tech/project/mne-icalabel)
[](https://anaconda.org/conda-forge/mne-icalabel/)
[](https://anaconda.org/conda-forge/mne-icalabel)
[](https://anaconda.org/conda-forge/mne-icalabel)
[](https://joss.theoj.org/papers/d91770e35a985ecda4f2e1f124977207)
This repository is a conversion of the popular Matlab-based
[ICLabel](https://github.com/sccn/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](https://mne.tools/stable/generated/mne.io.Raw.html) or
[Epochs](https://mne.tools/stable/generated/mne.Epochs.html) object and an ICA instance using the
[ICA decomposition](https://mne.tools/stable/generated/mne.preprocessing.ICA.html)
available in MNE-Python.
```
from mne_icalabel import label_components
# 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](https://mne.tools/mne-icalabel/stable/index.html) documentation.
[Dev version](https://mne.tools/mne-icalabel/dev/index.html) 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](https://mne.tools/mne-icalabel/stable/install.html) page.
To get the latest (development) version, using [git](https://git-scm.com/), 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](https://github.com/mne-tools/mne-icalabel/archive/main.zip).
# Contributing
If you are interested in contributing, please read the
[contributing guidelines](https://github.com/mne-tools/mne-icalabel/blob/main/CONTRIBUTING.md).
# Getting Help
[<img alt="MNE Forum" src="https://user-images.githubusercontent.com/1681963/52239617-e2683480-289c-11e9-922b-5da55472e5b4.png" height=60/>](https://mne.discourse.group)
For any usage questions, please post to the
[MNE Forum](https://mne.discourse.group). 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.
Raw data
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"description": "# mne-icalabel\n\n[](https://github.com/astral-sh/ruff)\n[](https://github.com/psf/black)\n[](https://pycqa.github.io/isort/)\n[](https://codecov.io/gh/mne-tools/mne-icalabel)\n[](https://github.com/mne-tools/mne-icalabel/actions/workflows/pytest.yaml)\n[](https://github.com/mne-tools/mne-icalabel/actions/workflows/doc.yaml)\n[](https://pypi.org/project/mne-icalabel/)\n[](https://pepy.tech/project/mne-icalabel)\n[](https://anaconda.org/conda-forge/mne-icalabel/)\n[](https://anaconda.org/conda-forge/mne-icalabel)\n[](https://anaconda.org/conda-forge/mne-icalabel)\n[](https://joss.theoj.org/papers/d91770e35a985ecda4f2e1f124977207)\n\nThis repository is a conversion of the popular Matlab-based\n[ICLabel](https://github.com/sccn/ICLabel) classifier for Python.\nIn addition, `mne-icalabel` provides extensions and improvements in the form of other models.\n\n# Why?\n\nEEG and MEG recordings include artifacts, such as heartbeat, eyeblink, muscle, and movement activity.\nIndependent component analysis (ICA) is a common method to remove artifacts, but typically relies on manual\nannotations labelling which independent components (IC) reflect noise and which reflect brain activity.\n\nThis package aims at automating this process, using the popular MNE-Python API for EEG, MEG and iEEG data.\n\n# Basic Usage\n\nMNE-ICALabel estimates the labels of ICA components given\na MNE-Python [Raw](https://mne.tools/stable/generated/mne.io.Raw.html) or\n[Epochs](https://mne.tools/stable/generated/mne.Epochs.html) object and an ICA instance using the\n[ICA decomposition](https://mne.tools/stable/generated/mne.preprocessing.ICA.html)\navailable in MNE-Python.\n\n```\nfrom mne_icalabel import label_components\n\n# assuming you have a Raw and ICA instance previously fitted\nlabel_components(raw, ica, method='iclabel')\n```\n\nThe only current available method is `'iclabel'`.\n\n# Documentation\n\n[Stable version](https://mne.tools/mne-icalabel/stable/index.html) documentation.\n[Dev version](https://mne.tools/mne-icalabel/dev/index.html) documentation.\n\n# Installation\n\nThe current stable release of `mne-icalabel` can be installed with pip, for example, by running:\n\n pip install mne-icalabel\n\nFor further details about installation, see the\n[install](https://mne.tools/mne-icalabel/stable/install.html) page.\n\nTo get the latest (development) version, using [git](https://git-scm.com/), open a terminal and type:\n\n git clone git://github.com/mne-tools/mne-icalabel.git\n cd mne-icalabel\n pip install -e .\n\nThe development version can also be installed directly using pip:\n\n pip install https://api.github.com/repos/mne-tools/mne-icalabel/zipball/main\n\nAlternatively, you can also download a\n[zip file of the latest development version](https://github.com/mne-tools/mne-icalabel/archive/main.zip).\n\n# Contributing\n\nIf you are interested in contributing, please read the\n[contributing guidelines](https://github.com/mne-tools/mne-icalabel/blob/main/CONTRIBUTING.md).\n\n# Getting Help\n\n[<img alt=\"MNE Forum\" src=\"https://user-images.githubusercontent.com/1681963/52239617-e2683480-289c-11e9-922b-5da55472e5b4.png\" height=60/>](https://mne.discourse.group)\n\nFor any usage questions, please post to the\n[MNE Forum](https://mne.discourse.group). Be sure to add the `mne-icalabel` tag to\nyour question.\n\n# Citing\n\nIf you use the ``mne-icalabel``, please consider citing our paper:\n\n```\n@article{Li2022,\n title = {MNE-ICALabel: Automatically annotating ICA components with ICLabel in Python},\n volume = {7},\n ISSN = {2475-9066},\n url = {http://dx.doi.org/10.21105/joss.04484},\n DOI = {10.21105/joss.04484},\n number = {76},\n journal = {Journal of Open Source Software},\n publisher = {The Open Journal},\n author = {Li, Adam and Feitelberg, Jacob and Saini, Anand Prakash and H\\\"{o}chenberger, Richard and Scheltienne, Mathieu},\n year = {2022},\n month = aug,\n pages = {4484}\n}\n```\n\nAnd the paper associated to the model used:\n\n- **ICLabel**\n\n```\n@article{PionTonachini2019,\n title = {ICLabel: An automated electroencephalographic independent component classifier, dataset, and website},\n volume = {198},\n ISSN = {1053-8119},\n url = {http://dx.doi.org/10.1016/j.neuroimage.2019.05.026},\n DOI = {10.1016/j.neuroimage.2019.05.026},\n journal = {NeuroImage},\n publisher = {Elsevier BV},\n author = {Pion-Tonachini, Luca and Kreutz-Delgado, Ken and Makeig, Scott},\n year = {2019},\n month = sep,\n pages = {181\u2013197}\n}\n```\n\nFuture versions of the software are aimed at improved models and may have different papers associated with it.\n",
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