Metadata-Version: 2.1
Name: mne-icalabel
Version: 0.1
Summary: MNE-ICALabel: Automatic labeling of ICA components from MEG, EEG and iEEG data with MNE.
Home-page: https://github.com/mne-tools/mne-icalabel
Author: mne-icalabel developers
Maintainer: Adam Li, Jacob Feitelberg, Patrick Myers
Maintainer-email: ali39@jhu.edu
License: BSD-3-Clause
Project-URL: Documentation, https://mne.tools/mne-icalabel
Project-URL: Bug Reports, https://github.com/mne-tools/mne-icalabel/issues
Project-URL: Source, https://github.com/mne-tools/mne-icalabel
Keywords: meg,eeg,ieeg,neuroscience,neuroimaging
Platform: any
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: ~=3.7
Description-Content-Type: text/markdown; charset=UTF-8
Provides-Extra: full
License-File: LICENSE

# mne-icalabel

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This repository is a conversion of the popular ICLabel classifier for Python. In addition, we provide
improvements in the form of other models.

# Why?

Scalp EEG is inherently noisy comprised commonly with heartbeat, eyeblink, muscle and movement artifacts.
Independent component analysis (ICA) is a common method to remove artifacts, but rely on a human manually
annotating with independent components (IC) are noisy and which are brain signal.

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

# Basic Usage

MNE-ICALabel will estimate the labels of the 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

To get the latest code 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 .

or one can install directly using pip

    pip install --user -U 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/main/CONTRIBUTING.md).


# Forum

Please visit the MNE forum to ask relevant questions.

https://mne.discourse.group


