An Ultra-low-power Real-time Machine Learning based fNIRS Motion Artefacts Detection

Ercan, R., Xia, Y., Zhao, Y. et al. (3 more authors) (Accepted: 2024) An Ultra-low-power Real-time Machine Learning based fNIRS Motion Artefacts Detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. ISSN 1063-8210 (In Press)

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Item Type: Article
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Dates:
  • Accepted: 13 January 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 24 Jan 2024 12:42
Last Modified: 24 Apr 2024 09:21
Status: In Press
Publisher: IEEE

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