fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control
Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducib...
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sg-ntu-dr.10356-1528972023-03-05T15:34:35Z fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control Gabrieli, Giulio Bizzego, Andrea Neoh, Michelle Jin Yee Esposito, Gianluca School of Social Sciences Lee Kong Chian School of Medicine (LKCMedicine) Psychology Social and Affective Neuroscience Lab Social sciences::Psychology::Experimental psychology Science::Medicine::Computer applications fNIRS Machine Learning Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control. Nanyang Technological University Published version This research was supported by grants from the NAP SUG to GE (M4081597, 2015-2021). 2021-10-18T01:55:54Z 2021-10-18T01:55:54Z 2021 Journal Article Gabrieli, G., Bizzego, A., Neoh, M. J. Y. & Esposito, G. (2021). fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control. Applied Sciences, 11(20), 9531-. https://dx.doi.org/10.3390/app11209531 2076-3417 https://hdl.handle.net/10356/152897 10.3390/app11209531 20 11 9531 en M4081597 (2015-2021) Applied Sciences 10.21979/N9/C8VYZG © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
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Social sciences::Psychology::Experimental psychology Science::Medicine::Computer applications fNIRS Machine Learning Gabrieli, Giulio Bizzego, Andrea Neoh, Michelle Jin Yee Esposito, Gianluca fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
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Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise in the application of the fNIRS in neuroscience experimental designs, the processing of fNIRS data remains characterized by a high number of heterogeneous approaches, implicating the scientific reproducibility and interpretability of the results. For example, a manual inspection is still necessary to assess the quality and subsequent retention of collected fNIRS signals for analysis. Machine Learning (ML) approaches are well-positioned to provide a unique contribution to fNIRS data processing by automating and standardizing methodological approaches for quality control, where ML models can produce objective and reproducible results. However, any successful ML application is grounded in a high-quality dataset of labeled training data, and unfortunately, no such dataset is currently available for fNIRS signals. In this work, we introduce fNIRS-QC, a platform designed for the crowd-sourced creation of a quality control fNIRS dataset. In particular, we (a) composed a dataset of 4385 fNIRS signals; (b) created a web interface to allow multiple users to manually label the signal quality of 510 10 s fNIRS segments. Finally, (c) a subset of the labeled dataset is used to develop a proof-of-concept ML model to automatically assess the quality of fNIRS signals. The developed ML models can serve as a more objective and efficient quality control check that minimizes error from manual inspection and the need for expertise with signal quality control. |
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School of Social Sciences |
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School of Social Sciences Gabrieli, Giulio Bizzego, Andrea Neoh, Michelle Jin Yee Esposito, Gianluca |
format |
Article |
author |
Gabrieli, Giulio Bizzego, Andrea Neoh, Michelle Jin Yee Esposito, Gianluca |
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Gabrieli, Giulio |
title |
fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
title_short |
fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
title_full |
fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
title_fullStr |
fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
title_full_unstemmed |
fNIRS-QC : crowd-sourced creation of a dataset and machine learning model for fNIRS quality control |
title_sort |
fnirs-qc : crowd-sourced creation of a dataset and machine learning model for fnirs quality control |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/152897 |
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1759857446880280576 |