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This dataset quantifies the impact of hair and skin characteristics on functional near-infrared spectroscopy (fNIRS) signal quality, with a focus on enhancing inclusivity in neuroimaging research. The study examines how individual physiological variations affect fNIRS measurements across diverse participant populations, providing empirical evidence for optimizing signal acquisition and preprocessing in this neuroimaging modality.

Participants

115

Size

1.38 GB

Version

v1.0.0

Updated

Jul 10, 2026

…the Advanced MRI section, LFMI, NINDS, National Institutes of Health, in approximately…

Participants

16

Channels

63

Size

2.20 TB

Version

v1.0.0

Updated

Jul 22, 2026

…neurotypical.git This work was funded by NINDS under award number R21NS133605.

Participants

19

Channels

70 (10-10)

Citations

12

Size

88.7 GB

Version

v1.0.0

Updated

Jul 10, 2026

…was supported by NIH grants: - NINDS RF1 NS126144 - NIBIB 1U18EB02935 ## Contact Li…

Participants

2

Size

5.07 GB

Version

v1.0.0

Updated

Sep 8, 2026

…Neurorehabilitation Section (HCPS) in the NINDS Intramural Research Program. The primary aim…

Participants

31

Citations

3

Size

95.8 GB

Version

v1.0.0

Updated

Aug 19, 2026