Transformer-based deep learning model for predicting fNIRS short-channel signals
Transformer-based models accurately reconstruct extracerebral hemodynamic signals from long-separation fNIRS data, providing a virtual alternative to physical short channels and supporting standardized, hardware-independent preprocessing.
Authors
- Felix Scholkmann (ORCID: https://orcid.org/0000-0002-1748-4852)
- Sabino Guglielmini (ORCID: https://orcid.org/0000-0001-6493-2753)
- Martin Wolf
- Vittoria Banchieri
Institutions
- University of Zurich (CH)
- University Hospital of Zurich (CH)
Publication Details
- Journal
- Neurophotonics
- Published
- 2025-11-14
- DOI
- https://doi.org/10.1117/1.nph.12.4.045008
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00