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Benchmarking framework for machine learning with fNIRS

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Benchmarking framework for machine learning with fNIRS

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Journal article
BenchNIRS source code
Install BenchNIRS
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DOI License Pipeline PyPI version

Example of figure


  • loading of open access datasets
  • signal processing and feature extraction on fNIRS data
  • training, fine-tuning and evaluation of machine learning models (including deep learning)
  • production of training graphs, metrics and other useful figures for evaluation
  • benchmarking and comparison of machine learning models
  • supervised, self-supervised and transfer learning
  • much more!


The documentation of the framework with examples can be found here.

Recommendation checklist

A checklist of recommendations towards best practices for machine learning with fNIRS can be found here. We welcome contributions from the community in order to improve it, please see below for more information on how to contribute.

Setting up BenchNIRS

  1. Download and install Python 3.9 or greater, for example with Miniconda.

  2. To install the package with pip (cf. PyPI), open a terminal (eg. Anaconda Prompt) and type:

pip install benchnirs
  1. Download the datasets (see below).

Alternatively to install from source in development mode, download and unzip the repository (or clone it with Git), and run

Downloading the datasets

  • Herff et al. 2014 (n-back task): you can download the dataset by making a request here.
  • Shin et al. 2018 (n-back and word generation tasks): you can download the dataset here.
  • Shin et al. 2016 (mental arithmetic task): you can download the dataset by filling out the form here. Then click on NIRS_01-29 to download the fNIRS data.
  • Bak et al. 2019 (motor execution task): you can download the dataset here.

Keeping BenchNIRS up to date

To update BenchNIRS to the latest version with pip, open a terminal (eg. Anaconda Prompt) and type:

pip install --upgrade benchnirs


A set of example scripts showing how to use the framework can be found here.

Simple use case

BenchNIRS enables to evaluate your model in Python with simplicity on an open access dataset supported:

import benchnirs as bn

epochs = bn.load_dataset('bak_2019_me', path)
data = bn.process_epochs(epochs['right', 'left', 'foot'])
results = bn.deep_learn(*data, my_model)


Contributing to the repository

Contributions from the community to this repository are highly appreciated. We are mainly interested in contributions to:

  • improving the recommendation checklist
  • adding support for new open access datasets
  • adding support for new machine learning models
  • adding more fNIRS signal processing techniques
  • improving the documentation
  • tracking bugs

Contributions are encouraged under the form of issues (for reporting bugs or requesting new features) and merge requests (for fixing bugs and implementing new features). Please refer to this tutorial for creating merge requests from a fork of the repository.


If you are using BenchNIRS, please cite this article:

  title={Benchmarking framework for machine learning classification from fNIRS data},
  author={Benerradi, Johann and Clos, Jeremie and Landowska, Aleksandra and Valstar, Michel F and Wilson, Max L},
  journal={Frontiers in Neuroergonomics},
  publisher={Frontiers Media},

If you are using the datasets of the framework, please also cite those related works:

Herff et al. 2014

	title={Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS},
	author={Herff, Christian and Heger, Dominic and Fortmann, Ole and Hennrich, Johannes and Putze, Felix and Schultz, Tanja},
	journal={Frontiers in human neuroscience},

Shin et al. 2018

	title={Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset},
	author={Shin, Jaeyoung and Von L{\"u}hmann, Alexander and Kim, Do-Won and Mehnert, Jan and Hwang, Han-Jeong and M{\"u}ller, Klaus-Robert},
	journal={Scientific data},
	publisher={Nature Publishing Group}

Shin et al. 2016

	title={Open access dataset for EEG+NIRS single-trial classification},
	author={Shin, Jaeyoung and von L{\"u}hmann, Alexander and Blankertz, Benjamin and Kim, Do-Won and Jeong, Jichai and Hwang, Han-Jeong and M{\"u}ller, Klaus-Robert},
	journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},

Bak et al. 2019

	title={Open-Access fNIRS Dataset for Classification of Unilateral Finger-and Foot-Tapping},
	author={Bak, SuJin and Park, Jinwoo and Shin, Jaeyoung and Jeong, Jichai},
	publisher={Multidisciplinary Digital Publishing Institute}

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