Quantifying the impact of hair and skin characteristics on fNIRS signal quality for enhanced inclusivity

Abstract

Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging method owing to its non-invasive nature and adaptability to real-world settings. However, fNIRS signal quality is sensitive to individual differences in biophysical factors such as hair and skin characteristics, which can considerably impact the absorption and scattering of near-infrared light. If not properly addressed, these factors risk biasing fNIRS research by disproportionately affecting signal quality across diverse populations. Here we quantify the impact of hair properties and skin pigmentation, as well as head size, sex and age, on signal quality in n = 115 individuals. We provide recommendations for fNIRS researchers, including a suggested metadata table and guidance for cap and optode configurations, hair management techniques and strategies to optimize data collection across varied participants. This research will help to guide future hardware advances and methodological standards to overcome barriers to inclusivity in fNIRS studies.