NiReject: toward automated bad channel detection in functional near-infrared spectroscopy
This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection.
Authors
- Meryem A. Yücel (ORCID: https://orcid.org/0000-0002-4291-2847)
- Kerstin Konrad (ORCID: https://orcid.org/0000-0001-9039-2615)
- Vanessa Reindl (ORCID: https://orcid.org/0000-0001-7791-5346)
- Lena Mehlem
- Christian Gerloff (ORCID: https://orcid.org/0000-0003-2718-2089)
Institutions
- Boston University (US)
- Harvard University (US)
- Forschungszentrum Jülich (DE)
- Nanyang Technological University (SG)
- University of Cambridge (GB)
- Massachusetts General Hospital (US)
- NeoPhotonics (United States) (US)
- Universitätsklinikum Aachen (DE)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- Jülich Aachen Research Alliance (DE)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Neurophotonics
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1117/1.nph.11.4.045008
- Citations
- 2
- Primary Topic
- Electrochemical Analysis and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.34
Funders
- RWTH Aachen University