Choosing the best motion artefact correction: simplified and advanced how-to guides using QT-NIRS

Abstract

SignificanceSelecting the appropriate motion artefact (MA) correction method for functional near-infrared spectroscopy (fNIRS) data is quite challenging, particularly in light of the need for standardised practice, replication, and transparency in the field. A clear framework for making measurable and replicable decisions is therefore essential. AimThis paper proposes a guide based on an open-source data quality assessment tool (QT-NIRS) that enables a transparent and evidence-driven choice of MA correction method. ApproachWe present the guide in two approaches: a simplified version that is easy to run for beginners, and an advanced version providing more informative output at the cost of additional computations and minor changes to the original QT-NIRS code. Due to its high flexibility and within-subject nature, the method is applicable across samples with varied characteristics. ResultsWe applied the guide to two challenging datasets from 60 British preschoolers (mean age = 3.94 years, SD = 0.49) and 39 South African school children (mean age = 12.00 years, SD = 0.51). Both simplified and advanced approaches supported similar MA correction methods. ConclusionsWhile both approaches can be used interchangeably, we recommend the advanced approach when possible due to its more informative and straightforward output, and advise caution when using the simplified version.