Deep learning-based motion artifact removal in functional near-infrared spectroscopy

Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that h...

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Vydáno v:	Neurophotonics (Print) Ročník 9; číslo 4; s. 041406
Hlavní autoři:	Gao, Yuanyuan, Chao, Hanqing, Cavuoto, Lora, Yan, Pingkun, Kruger, Uwe, Norfleet, Jack E., Makled, Basiel A., Schwaitzberg, Steven, De, Suvranu, Intes, Xavier
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Society of Photo-Optical Instrumentation Engineers 01.10.2022 SPIE S P I E - International Society for
Témata:	Data processing Datasets Deep learning Infrared spectroscopy Methods Neural networks Neuroimaging Special Section on Computational Approaches for Neuroimaging Spectrum analysis deep learning denoising autoencoder functional near-infrared spectroscopy motion artifact
ISSN:	2329-423X, 2329-4248
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