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Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset.

, , , , , and . IWOCL, page 29:1. ACM, (2023)

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An Ultralow-Power Real-Time Machine Learning Based fNIRS Motion Artifacts Detection., , , , , and . IEEE Trans. Very Large Scale Integr. Syst., 32 (4): 763-773 (April 2024)FPL Demo: A Learning-Based Motion Artefact Detector for Heterogeneous Platforms., , , , , and . FPL, page 366. IEEE, (2023)Wearable, Integrated EEG-fNIRS Technologies: A Review., , , and . Sensors, 21 (18): 6106 (2021)On-Probe Neural Interface ASIC for Combined Electrical Recording and Optogenetic Stimulation., , , , , , , , , and 2 other author(s). IEEE Trans. Biomed. Circuits Syst., 12 (3): 576-588 (2018)An implantable optrode with Self-diagnostic function in 0.35µm CMOS for optical neural stimulation., , and . BioCAS, page 244-247. IEEE, (2014)Edge Acceleration for Machine Learning-based Motion Artifact Detection on fNIRS Dataset., , , , , and . IWOCL, page 29:1. ACM, (2023)Intelligent machines work in unstructured environments by differential neural computing., , , , , , , , , and 1 other author(s). CoRR, (2023)A Scalable Optoelectronic Neural Probe Architecture With Self-Diagnostic Capability., , , , , and . IEEE Trans. Circuits Syst. I Regul. Pap., 65-I (8): 2431-2442 (2018)An 8100 pixel optoelectronic array for optogenetic retinal prosthesis., , , , , and . BioCAS, page 352-355. IEEE, (2014)A CMOS-based neural implantable optrode for optogenetic stimulation and electrical recording., , , , , , and . BioCAS, page 1-4. IEEE, (2015)