Improving Performance in fNIRS Single Trial Analysis: Multidisciplinary Opportunities and Perspective

Advancements in wearable technologies and signal analysis are bringing functional Near-Infrared Spectroscopy (fNIRS) to the forefront of mobile non-invasive brain-computer interface research. As it gains main-stream attention, Diffuse Optical Tomography (DOT), a high-density fNIRS variant, shows gre...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:The ... International Winter Conference on Brain-Computer Interface s. 1 - 3
Hlavní autoři: von Luhmann, Alexander, Middell, Eike, Fischer, Thomas, Tesch, Christian, Siddique, Bilal, Zimmermann, Bernhard B., Moradi, Shakiba, Boas, David A., Muller, Klaus-Robert
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 24.02.2025
Témata:
Brain-computer interfaces
classification
DOT
fNIRS
Functional near-infrared spectroscopy
Instruments
Machine learning
Optical imaging
Physiology
physiology removal
single trial analysis
Technological innovation
Tomography
US Department of Transportation
Usability
wearable
ISSN:2572-7672
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Advancements in wearable technologies and signal analysis are bringing functional Near-Infrared Spectroscopy (fNIRS) to the forefront of mobile non-invasive brain-computer interface research. As it gains main-stream attention, Diffuse Optical Tomography (DOT), a high-density fNIRS variant, shows great promise by enhancing spatial resolution and brain-imaging contrast while maintaining the ease of use and usability of optical brain imaging techniques. However, to fully unlock the potential of mobile fNIRS and DOT, persisting challenges in extracting meaningful task-evoked hemodynamic signals amidst systemic physiological noise must be overcome, particularly for single-trial analyses. We briefly review the recent advances in wearable fNIRS/DOT instrumentation and highlight multidisciplinary opportunities to improve single trial decoding performance by combining advances in wearable DOT instrumentation with model-driven best practices from the fNIRS neuroscience community and data-driven innovations in multimodal machine learning. Finally, we introduce Cedalion, our recently launched open-source Python toolbox for state-of-the-art fNIRS/DOT analysis and multimodal machine learning.
ISSN:2572-7672
DOI:10.1109/BCI65088.2025.10931754