This is a library to assist with processing near-infrared spectroscopy data with MNE.
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
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.
This library is built on top of other great packages. If you use MNE-NIRS you should also acknowledge these packages.
Until there is a journal article specifically on MNE-NIRS, please cite this article.