A benchmarking framework for improving machine learning with fNIRS neuroimaging data - Benerradi, Johann (2024)
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology that presents attractive advantages such as a good compromise between temporal resolution, spatial resolution, and portability. Thanks to its properties and relative robustness to motion, it is often used in naturalistic settings and one of its key domains of application is the study of working memory and mental workload. However fNIRS is less popular and tested than other technologies such as electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) which can nowadays be used in a clinical context. As such, there is no real consensus yet in the research community as to how fNIRS data should be used and analysed. While efforts to establish best practices with fNIRS have been published, there are still no community standards for using machine learning with 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 methodology practices or reporting with missing details. These issues make it hard to evaluate the performance of models when it comes to choosing them for brain-computer interface (BCI) applications.