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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Vydané v:Neurophotonics (Print) Ročník 9; číslo 4; s. 041406
Hlavní autori: 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:English
Vydavateľské údaje: United States Society of Photo-Optical Instrumentation Engineers 01.10.2022
SPIE
S P I E - International Society for
Predmet:
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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Shrnutí: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 have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters. Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal. Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences. Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency. Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.
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ISSN:2329-423X
2329-4248
DOI:10.1117/1.NPh.9.4.041406