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

Nature Human Behaviour (2025)Cite this article

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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.

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Fig. 1: Optode array design, and violin plots illustrating the relationship between hair characteristics and the corrected signal mean on the side and back of the head.
Fig. 2: Violin plots illustrating the relationship between hair characteristics and the corrected signal mean.
Fig. 3: Violin plots illustrating the relationships between skin pigmentation or participant-level factors and corrected signal mean.
Fig. 4: Effect sizes from regression models reveal the contributions of predictor variables to multiple fNIRS signal quality metrics.
Fig. 5: Scatter plot of the uncorrected signal mean in individual channels across all participants versus the combined hair–skin metric.
Fig. 6: Cross-correlation heat map displaying the relationships between various participant-level factors.

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Data availability

The data used in this study are available via OpenNeuro at https://openneuro.org/datasets/ds006377/.

Code availability

The code used in this study is available via OpenNeuro at https://openneuro.org/datasets/ds006377 in the InclusionStudy/code directory and via GitHub at https://github.com/mayucel/InclusionStudy.

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Acknowledgements

This research was supported by Meta Reality Labs (formerly Facebook Technologies, LLC) as part of the Engineering Approaches to Responsible Neural Interface Design Initiative (M.A.Y.). The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We acknowledge NIH U01EB0239856 (M.A.Y., D.B., S.K., D.C.S., A.C.-G. and T.D.E.), NSF Research Traineeship Program (DGE-1633516) (E.C.), the Netherlands Organization for Scientific Research (NWO Vidi-Grant VI.Vidi.191.210) (B.S.) and the German Federal Ministry of Education and Research (BIFOLD24B) (A.v.L.). We thank the NIRx team for their valuable support in guiding us on source power correction and helping us locate the relevant information in the acquired data.

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Authors and Affiliations

  1. Neurophotonics Center, Boston University, Boston, MA, USA

    Meryem A. Yücel, Jessica E. Anderson, De’Ja Rogers, Parisa Hajirahimi, Parya Farzam, Yuanyuan Gao, Rini I. Kaplan, Emily J. Braun, Nishaat Mukadam, Sudan Duwadi, Laura Carlton, David Beeler, Erin Carpenter, Jaimie Girnis, Vaibhav Tripathi, Yiwen Zhang, Alexander von Lühmann, David C. Somers, Alice Cronin-Golomb, Swathi Kiran, Terry D. Ellis & David A. Boas

  2. Department of Biomedical Engineering, Boston University, Boston, MA, USA

    Meryem A. Yücel, Jessica E. Anderson, De’Ja Rogers, Parisa Hajirahimi, Parya Farzam, Yuanyuan Gao, Sudan Duwadi, Laura Carlton, Yiwen Zhang, Alexander von Lühmann & David A. Boas

  3. Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA

    Rini I. Kaplan, Nishaat Mukadam, David Beeler, Lindsay K. Butler, John Wilson, Vaibhav Tripathi, David C. Somers & Alice Cronin-Golomb

  4. Department of Speech, Language and Hearing Sciences, Boston University, Boston, MA, USA

    Emily J. Braun, Erin Carpenter & Swathi Kiran

  5. Department of Physical Therapy, Boston University, Boston, MA, USA

    Jaimie Girnis & Terry D. Ellis

  6. Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands

    Bettina Sorger

  7. Intelligent Biomedical Sensing Lab, Technical University Berlin, Berlin, Germany

    Alexander von Lühmann

  8. BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany

    Alexander von Lühmann

Authors
  1. Meryem A. Yücel
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Contributions

M.A.Y. and D.A.B. conceptualized the research question and framework. M.A.Y. led the project. M.A.Y., D.A.B., J.E.A., D.R. and Y.G. designed the experimental approach and protocols. J.E.A. performed the training of experimenters. P.H., P.F. and N.M. managed the recruitment of participants. J.E.A., D.R., P.H., P.F., R.I.K., E.J.B., N.M., S.D., L.C., D.B., L.K.B., E.C., J.G., J.W., V.T. and Y.Z. executed the experiments. M.A.Y. performed data analysis, statistical assessments and created the visualizations. A.v.L. conceptualized Fig. 5, with A.v.L. and M.A.Y. contributing to its analysis and visualization. M.A.Y. drafted the original manuscript. M.A.Y., D.A.B., B.S., A.v.L., E.C., J.E.A., D.R., R.I.K., E.J.B., N.M., A.C.-G. and T.D.E. reviewed, provided feedback and edited the manuscript. M.A.Y. secured funding for the research project. All authors approved the final version of the paper.

Corresponding author

Correspondence to Meryem A. Yücel.

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A.v.L. is currently consulting for NIRx Medical Technologies LLC/GmbH. The remaining authors declare no competing interests.

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Nature Human Behaviour thanks Leanne Hirshfield, Jasmine Kwasa and Michele Lacerenza for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Correlation analysis of Corrected Signal Mean on the forehead, side of the head and back of the head with participant-level factors
Full size table
Extended Data Table 2 Output of Multiple Linear Regression analysis for predictors of Corrected Signal Mean on the forehead, side of the head and back of the head
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Extended Data Table 3 Output of Multiple Linear Regression Analysis for Predictors of SCI at the forehead, side of the head and back of the head
Full size table
Extended Data Table 4 Multiple linear regression analysis results for factors influencing GLM output changes in changes in oxyhemoglobin concentration (∆HbO2) and t statistics during the ball-squeezing task
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Extended Data Table 5 Mean, standard deviation and the percent change from Run 1 to Run 2 of Uncorrected Signal Mean and SCI for the forehead, side of the head and back of the head
Full size table
Extended Data Table 6 Classification scales for hair characteristics including hair colour, type, and coarseness
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Extended Data Table 7 List of Skin, Hair, and Head Measurements with Definitions and Objectivity Levels
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Extended Data Table 8 Mean and standard deviation of hair characteristics on the side and back of the head
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Extended Data Table 9 Mean and standard deviation of whole-head hair characteristics, skin properties, and head measurements for the study population
Full size table

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Yücel, M.A., Anderson, J.E., Rogers, D. et al. Quantifying the impact of hair and skin characteristics on fNIRS signal quality for enhanced inclusivity. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02274-7

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