MNE-NIRS: Near-Infrared Spectroscopy Analysis
=============================================
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**MNE-NIRS** is an `MNE-Python <https://mne.tools>`_ compatible near-infrared spectroscopy processing package.
MNE-Python provides support for fNIRS analysis, this package extends that functionality and adds GLM analysis, helper functions, additional algorithms, data quality metrics, plotting, and file format support.
Documentation
-------------
Documentation for this project is hosted `here <https://mne-tools.github.io/mne-nirs>`_.
You can find a list of `examples within the documentation <https://mne.tools/mne-nirs/stable/auto_examples/index.html>`_.
Features
--------
.. features-start
MNE-NIRS and MNE-Python provide a wide variety of tools to use when processing fNIRS data including:
* Loading data from a `wide variety of devices <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_01_data_io.html>`_, including `SNIRF files <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_19_snirf.html>`_.
* `Apply 3D sensor locations <https://mne.tools/stable/overview/implementation.html#supported-formats-for-digitized-3d-locations>`_ from common digitisation systems such as Polhemus.
* Standard preprocessing including `optical density calculation and Beer-Lambert Law conversion <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_15_waveform.html#id2>`_, filtering, etc.
* Data quality metrics including `scalp coupling index <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_15_waveform.html#id3>`_ and `peak power <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_22_quality.html#peak-power>`_.
* GLM analysis with a wide variety of customisation including `including FIR <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_13_fir_glm.html>`_ or `canonical HRF <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_11_hrf_measured.html>`_ analysis, higher order `autoregressive noise models <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_10_hrf_simulation.html#using-autoregressive-models-in-the-glm-to-account-for-noise-structure>`_, `short channel regression, region of interest analysis <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_11_hrf_measured.html>`_, etc.
* Visualisation tools for all stages of processing from raw data to processed waveforms, GLM result visualisation, including both sensor and cortical surface projections.
* Data cleaning functions including popular short channel techniques and negative correlation enhancement.
* Group level analysis using `(robust) linear mixed effects models <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_12_group_glm.html>`_ and `waveform averaging <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_16_waveform_group.html>`_.
* And much more! Check out the documentation `examples <https://mne.tools/mne-nirs/stable/auto_examples/index.html>`__ and the API `for more details <https://mne.tools/mne-nirs/stable/api.html>`_.
.. features-end
Contributing
------------
Contributions are welcome (more than welcome!). Contributions can be feature requests, improved documentation, bug reports, code improvements, new code, etc. Anything will be appreciated. *Note*: this package adheres to the same contribution `standards as MNE <https://mne.tools/stable/install/contributing.html>`_.
Acknowledgements
----------------
This package is built on top of many other great packages. If you use MNE-NIRS you should also acknowledge these packages.
MNE-Python: https://mne.tools/dev/overview/cite.html
Nilearn: http://nilearn.github.io/authors.html#citing
statsmodels: https://www.statsmodels.org/stable/index.html#citation
Until there is a journal article specifically on MNE-NIRS, please cite `this article <https://doi.org/10.1117/1.NPh.8.2.025008>`_.
Docker
------
To start a jupyter lab server with a specified MNE-NIRS version, and mount a local directory on a mac or nix computer use:
.. code-block:: console
docker run -p 8888:8888 -v `pwd`:/home/mne_user ghcr.io/mne-tools/mne-nirs:v0.1.2 jupyter-lab --ip="*"
Or on windows:
.. code-block:: console
docker run -p 8888:8888 -v %cd%:/home/mne_user ghcr.io/mne-tools/mne-nirs:v0.1.2 jupyter-lab --ip="*"
Raw data
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"description": "MNE-NIRS: Near-Infrared Spectroscopy Analysis\n=============================================\n\n.. image:: https://img.shields.io/badge/docs-master-brightgreen\n :target: https://mne.tools/mne-nirs/\n \n.. image:: https://github.com/mne-tools/mne-nirs/workflows/linux%20/%20pip/badge.svg\n :target: https://github.com/mne-tools/mne-nirs/actions?query=workflow%3A%22linux+%2F+pip%22\n \n.. image:: https://github.com/mne-tools/mne-nirs/workflows/macos%20/%20conda/badge.svg\n :target: https://github.com/mne-tools/mne-nirs/actions?query=workflow%3A%22macos+%2F+conda%22\n \n.. image:: https://github.com/mne-tools/mne-nirs/workflows/linux%20/%20conda/badge.svg\n :target: https://github.com/mne-tools/mne-nirs/actions?query=workflow%3A%22linux+%2F+conda%22\n \n.. image:: https://codecov.io/gh/mne-tools/mne-nirs/branch/main/graph/badge.svg\n :target: https://codecov.io/gh/mne-tools/mne-nirs\n \n.. image:: https://badge.fury.io/py/mne-nirs.svg\n :target: https://badge.fury.io/py/mne-nirs\n\n**MNE-NIRS** is an `MNE-Python <https://mne.tools>`_ compatible near-infrared spectroscopy processing package.\n\nMNE-Python provides support for fNIRS analysis, this package extends that functionality and adds GLM analysis, helper functions, additional algorithms, data quality metrics, plotting, and file format support.\n\n\nDocumentation\n-------------\n\nDocumentation for this project is hosted `here <https://mne-tools.github.io/mne-nirs>`_.\n\nYou can find a list of `examples within the documentation <https://mne.tools/mne-nirs/stable/auto_examples/index.html>`_.\n\n\nFeatures\n--------\n\n.. features-start\n\nMNE-NIRS and MNE-Python provide a wide variety of tools to use when processing fNIRS data including:\n\n* Loading data from a `wide variety of devices <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_01_data_io.html>`_, including `SNIRF files <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_19_snirf.html>`_.\n* `Apply 3D sensor locations <https://mne.tools/stable/overview/implementation.html#supported-formats-for-digitized-3d-locations>`_ from common digitisation systems such as Polhemus.\n* Standard preprocessing including `optical density calculation and Beer-Lambert Law conversion <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_15_waveform.html#id2>`_, filtering, etc.\n* Data quality metrics including `scalp coupling index <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_15_waveform.html#id3>`_ and `peak power <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_22_quality.html#peak-power>`_.\n* GLM analysis with a wide variety of customisation including `including FIR <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_13_fir_glm.html>`_ or `canonical HRF <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_11_hrf_measured.html>`_ analysis, higher order `autoregressive noise models <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_10_hrf_simulation.html#using-autoregressive-models-in-the-glm-to-account-for-noise-structure>`_, `short channel regression, region of interest analysis <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_11_hrf_measured.html>`_, etc.\n* Visualisation tools for all stages of processing from raw data to processed waveforms, GLM result visualisation, including both sensor and cortical surface projections.\n* Data cleaning functions including popular short channel techniques and negative correlation enhancement.\n* Group level analysis using `(robust) linear mixed effects models <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_12_group_glm.html>`_ and `waveform averaging <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_16_waveform_group.html>`_.\n* And much more! 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