Riemannian geometry boosts functional near-infrared spectroscopy-based brain-state classification accuracy
To our knowledge, we are the first to demonstrate that the proposed Riemannian-geometry-based classification approach is both powerful and viable for fNIRS data, substantially increasing the accuracy in binary and multi-class classification of brain activation patterns.
Authors
- Lisa Bastian (ORCID: https://orcid.org/0000-0001-5812-0556)
- Rainer Goebel (ORCID: https://orcid.org/0000-0003-1780-2467)
- Tim Näher (ORCID: https://orcid.org/0000-0003-1526-0035)
- Pascal Fries (ORCID: https://orcid.org/0000-0002-4270-1468)
- Bettina Sorger (ORCID: https://orcid.org/0000-0003-1393-3144)
- Anna Vorreuther (ORCID: https://orcid.org/0009-0006-3556-2486)
Institutions
- University of Stuttgart (DE)
- Stuttgart Technical University of Applied Sciences (DE)
- Maastricht University (NL)
- Max Planck Institute for Biological Cybernetics (DE)
- University of Tübingen (DE)
Publication Details
- Journal
- Neurophotonics
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1117/1.nph.12.4.045002
- Primary Topic
- Optical Imaging and Spectroscopy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00