Optimizing short-channel regression in fNIRS: an empirical evaluation with ecological audiovisual stimuli
Overall, our findings highlight the essential role of SC regression in recovering physiologically meaningful signals in fNIRS and recommend including all available SC channels within the GLM, coupled with orthogonalization techniques, as a generalizable best practice for denoising across hardware configurations.
Authors
- Olivier Déguine (ORCID: https://orcid.org/0000-0003-4174-2580)
- Kuzma Strelnikov (ORCID: https://orcid.org/0000-0002-6613-2300)
- Pascal Barone
- Jérémie Ginzburg (ORCID: https://orcid.org/0000-0003-4851-146X)
- Yann Lemaire (ORCID: https://orcid.org/0009-0009-4375-1502)
- Anne Caclin
Institutions
- Université Claude Bernard Lyon 1 (FR)
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Centre de Recherche en Neurosciences de Lyon (FR)
- Centre de recherche cerveau et cognition (FR)
Publication Details
- Journal
- Neurophotonics
- Published
- 2026-02-17
- DOI
- https://doi.org/10.1117/1.nph.13.1.015012
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 0.00