Benchmarking framework for machine learning classification from fNIRS data.

ABSTRACT:

Background

While efforts to establish best practices with functional near infrared spectroscopy (fNIRS) signal processing have been published, there are still no community standards for applying machine learning to fNIRS data. Moreover, the lack of open source benchmarks and standard expectations for reporting means that published works often claim high generalisation capabilities, but with poor practices or missing details in the paper. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interfaces.

Methods

We present an open-source benchmarking framework, BenchNIRS, to establish a best practice machine learning methodology to evaluate models applied to fNIRS data, using five open access datasets for brain-comput

SUBMITTER: Benerradi J 

PROVIDER: S-EPMC10790918 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature