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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 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-start
MNE-NIRS and MNE-Python provide a wide variety of tools to use when processing fNIRS data including:
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.optical density calculation and Beer-Lambert Law conversion <https://mne.tools/mne-nirs/stable/auto_examples/general/plot_15_waveform.html#id2>
_, filtering, etc.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>
_.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.(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>
_.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
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>
_.
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>
_.
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="*"