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Related Experiment Video

Updated: Jul 5, 2025

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

Published on: October 6, 2023

Benchmarking framework for machine learning classification from fNIRS data.

Johann Benerradi¹, Jeremie Clos¹, Aleksandra Landowska¹

¹School of Computer Science, University of Nottingham, Nottingham, United Kingdom.

Frontiers in Neuroergonomics

|January 18, 2024

Summary

This summary is machine-generated.

A new framework, BenchNIRS, standardizes machine learning for functional near-infrared spectroscopy (fNIRS) brain-computer interfaces. Benchmarking reveals lower performance than often reported, emphasizing challenges in generalizing brain-computer interface models.

Keywords:

benchmarking deep learning fNIRS guidelines machine learning neural networks open access data

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Last Updated: Jul 5, 2025

Author Spotlight: Unlocking New Insights in fNIRS Studies - A Novel Framework for Inter-Brain Synchrony Analysis

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Published on: October 6, 2023

fMRI Validation of fNIRS Measurements During a Naturalistic Task

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Published on: June 15, 2015

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Area of Science:

Neuroscience
Machine Learning
Biomedical Engineering

Background:

Lack of standardized machine learning practices for functional near-infrared spectroscopy (fNIRS) data hinders reliable brain-computer interface (BCI) development.
Inconsistent reporting and absence of open-source benchmarks make evaluating BCI model generalizability difficult.

Purpose of the Study:

Establish a best-practice, open-source benchmarking framework (BenchNIRS) for evaluating machine learning models applied to fNIRS data.
Provide a standardized methodology for optimizing and assessing BCI model performance using fNIRS.

Main Methods:

Developed BenchNIRS, an open-source framework utilizing nested cross-validation on five public fNIRS datasets for BCI.
Benchmarked six machine learning models (LDA, SVM, kNN, ANN, CNN, LSTM) against varying training data and time window sizes.
Investigated performance differences between sliding window vs. epoch classification and personalized vs. generalized approaches.

Main Results:

Model performance on unseen data was generally lower than reported in literature, with minimal differences between models.
Benchmarking highlighted the persistent difficulty in achieving high generalizability for fNIRS-based BCIs.
The study identified factors influencing classification performance, including data quantity and temporal features.

Conclusions:

BenchNIRS offers a standardized tool for researchers to rigorously evaluate and compare machine learning models for fNIRS-based BCIs.
The findings underscore the need for transparent reporting and robust validation in fNIRS BCI research.
Recommendations are provided for methodology and reporting to advance the field of machine learning with fNIRS data.