MNE-NIRS

This is a library to assist with processing near-infrared spectroscopy data with MNE.

Installation

To install python and MNE follow these instructions.

Run the following code to install MNE-NIRS.

>>> pip install mne-nirs

To load MNE-NIRS add these lines to your script.

>>> import mne
>>> import mne_nirs

Usage

See the examples and API documentation.

Features

MNE-NIRS and MNE-Python provide a wide variety of tools to use when processing NIRS data including:

  • Loading data from a wide variety of devices, including SNIRF files.

  • Apply 3D sensor locations from common digitisation systems such as Polhemus.

  • Standard preprocessing including optical density calculation and Beer-Lambert Law conversion, filtering, etc.

  • Data quality metrics including scalp coupling index and peak power.

  • GLM analysis with a wide variety of cusomisation including including FIR or canonical HRF analysis, higher order autoregressive noise models, short channel regression, region of interest analysis, 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 and waveform averaging.

  • And much more! Check out the documentation examples and the API for more details.

Acknowledgements

This library is built on top of other great packages. If you use MNE-NIRS you should also acknowledge these packages.

MNE: 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.