An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.
- DOI
- 10.1109/tvlsi.2024.3356161
- Published
- 2024 Apr
- Container
- IEEE transactions on very large scale integration (VLSI) systems
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1109/tvlsi.2024.3356161,
title = {An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.},
author = {Ercan R and Xia Y and Zhao Y and Loureiro R and Yang S and Zhao H},
year = {2024},
journal = {IEEE transactions on very large scale integration (VLSI) systems},
doi = {10.1109/tvlsi.2024.3356161},
url = {https://doi.org/10.1109/tvlsi.2024.3356161}
}RIS
TY - JOUR TI - An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection. AU - Ercan R AU - Xia Y AU - Zhao Y AU - Loureiro R AU - Yang S AU - Zhao H PY - 2024 JO - IEEE transactions on very large scale integration (VLSI) systems DO - 10.1109/tvlsi.2024.3356161 UR - https://doi.org/10.1109/tvlsi.2024.3356161 ER -
APA
R, E., Y, X., Y, Z., R, L., S, Y., & H, Z. (2024). An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection.. IEEE transactions on very large scale integration (VLSI) systems. https://doi.org/10.1109/tvlsi.2024.3356161
Source records
- pubmed · retrieved 2026-11-10T15:05:50.643Z