All signals considered: Data quality partially explains inter-individual task differences in a large, open…

Abstract

Significance: High inter-subject variability and limited reproducibility in functional near-infrared spectroscopy (fNIRS) research may partly reflect global systemic physiology and signal quality differences, possibly distorting task-evoked hemodynamic responses. Aim: We investigate how signal quality relates to inter-subject variability in motor-task fNIRS responses and introduce a large, open, multi-task, near whole-head fNIRS dataset with extensive peripheral physiology and short-channel recordings. Approach: Fifty-seven participants completed resting-state, motor action, motor imagery, emotion recognition, visual, and auditory tasks during fNIRS recording. Peripheral measures included pulse oximetry, heart rate, blood oxygen saturation, respiration, room temperature, galvanic skin response, electrocardiogram, and electromyography. Signal quality was assessed using the scalp coupling index (SCI), coefficient of variation (CV), signal-to-noise ratio (SNR) and a spectral measure here coined the coupling SNR (cSNR). Results: Quality metrics were weakly to moderately correlated, except SNR and CV, which showed the expected inverse relationship. All quality metrics were significantly related to channel length and associated with task-related activation estimates. Group-level analyses validated activation in expected task-related regions. Conclusions: The assessed metrics capture complementary features of fNIRS signal quality and may help explain individual activation differences. The dataset provides a comprehensive, open resource enabling future evaluation of physiological correction methods and confound mitigation.

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BibTeX RIS

BibTeX

@misc{raible2026signals,
  title = {{All signals considered: Data quality partially explains inter-individual task differences in a large, open fNIRS dataset}},
  author = {S. Raible and J. Pereira and F. Kotsogiannis and B. Direito and T. Sousa and M. da Cunha Seiffert and R. Lavicka and V. Skeltona and D. Evenblij and A. Ciarlo and A. Heinecke and J. Gädtke and Z. Tipado and D. M. A. Mehler and S. H. Kohl and M. Castelo-Branco and R. Goebel and M. Lührs and B. Sorger},
  howpublished = {bioRxiv},
  year = {2026},
  month = jun,
  doi = {10.64898/2026.06.06.728412},
  url = {https://www.biorxiv.org/content/10.64898/2026.06.06.728412v2},
  note = {CC BY-NC},
}

RIS

TY  - UNPB
TI  - All signals considered: Data quality partially explains inter-individual task differences in a large, open fNIRS dataset
AU  - S. Raible
AU  - J. Pereira
AU  - F. Kotsogiannis
AU  - B. Direito
AU  - T. Sousa
AU  - M. da Cunha Seiffert
AU  - R. Lavicka
AU  - V. Skeltona
AU  - D. Evenblij
AU  - A. Ciarlo
AU  - A. Heinecke
AU  - J. Gädtke
AU  - Z. Tipado
AU  - D. M. A. Mehler
AU  - S. H. Kohl
AU  - M. Castelo-Branco
AU  - R. Goebel
AU  - M. Lührs
AU  - B. Sorger
PY  - 2026
DA  - 2026/06/06
PB  - bioRxiv
DO  - 10.64898/2026.06.06.728412
UR  - https://www.biorxiv.org/content/10.64898/2026.06.06.728412v2
AB  - Significance: High inter-subject variability and limited reproducibility in functional near-infrared spectroscopy (fNIRS) research may partly reflect global systemic physiology and signal quality differences, possibly distorting task-evoked hemodynamic responses. Aim: We investigate how signal quality relates to inter-subject variability in motor-task fNIRS responses and introduce a large, open, multi-task, near whole-head fNIRS dataset with extensive peripheral physiology and short-channel recordings. Approach: Fifty-seven participants completed resting-state, motor action, motor imagery, emotion recognition, visual, and auditory tasks during fNIRS recording. Peripheral measures included pulse oximetry, heart rate, blood oxygen saturation, respiration, room temperature, galvanic skin response, electrocardiogram, and electromyography. Signal quality was assessed using the scalp coupling index (SCI), coefficient of variation (CV), signal-to-noise ratio (SNR) and a spectral measure here coined the coupling SNR (cSNR). Results: Quality metrics were weakly to moderately correlated, except SNR and CV, which showed the expected inverse relationship. All quality metrics were significantly related to channel length and associated with task-related activation estimates. Group-level analyses validated activation in expected task-related regions. Conclusions: The assessed metrics capture complementary features of fNIRS signal quality and may help explain individual activation differences. The dataset provides a comprehensive, open resource enabling future evaluation of physiological correction methods and confound mitigation.
ER  -

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