An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection

Ercan, R., Xia, Y., Zhao, Y. et al. (3 more authors) (2024) An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 32 (4). 763 -773. ISSN 1063-8210

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Item Type: Article
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Keywords: Field-programmable gate array (FPGA), functional near-infrared spectroscopy (fNIRS), low power, machine learning, motion artifact detection, real time, support vector machines (SVMs)
Dates:
  • Published: April 2024
  • Published (online): 30 January 2024
  • 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 Jul 2024 14:31
Status: Published
Publisher: IEEE
Identification Number: 10.1109/TVLSI.2024.3356161
Open Archives Initiative ID (OAI ID):

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