Signal quality in fNIRS is shaped by biological, anatomical, technical, and procedural factors. Hair characteristics, scalp contact, optode placement, head shape, and participant comfort can all influence data quality. These effects may differ across regions of the head and across participant groups.
This stage focuses on transparent and consistent approaches to assessing, preprocessing, analyzing, and interpreting fNIRS data.
What this stage covers
- Region-aware signal-quality evaluation
- Real-time signal checks during setup
- Consistent quality criteria across participants and regions
- Avoiding relaxed thresholds for harder-to-measure regions
- Documenting channel retention and exclusion criteria
- Considering signal quality during interpretation
- Reporting whether data loss differs by region or participant characteristics
- Sensitivity analyses for preprocessing choices
- Transparent and reproducible preprocessing pipelines
Current resources
- Yücel et al. (2025), quantifying how hair and skin characteristics affect fNIRS signal quality: https://doi.org/10.1038/s41562-025-02274-7
- Kwasa et al. (2023), demographic reporting and phenotypic exclusion in fNIRS: https://doi.org/10.3389/fnins.2023.1086208
- Webb et al. (2022), addressing racial and phenotypic bias in human neuroscience methods: https://www.nature.com/articles/s41593-022-01046-0
Suggested additions
We welcome example signal-quality criteria, preprocessing pipelines, reporting templates for channel retention, tools for evaluating whether data loss differs by region or participant characteristics, example sensitivity analyses, and code for reproducible fNIRS quality-control workflows.
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