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: