Next Article in Journal
Profit-Driven Methodology for Servo Press Motion Selection under Material Variability
Next Article in Special Issue
Creating a Diagnostic Assistance System for Diseases in Kampo Medicine
Previous Article in Journal
Chemical Bond Formation between Vertically Aligned Carbon Nanotubes and Metal Substrates at Low Temperatures
Previous Article in Special Issue
An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
 
 
Article

fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control

1
Psychology Program, Nanyang Technological University, Singapore 639818, Singapore
2
Department of Psychology and Cognitive Science, University of Trento, 38068 Trento, Italy
3
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Alexander E. Hramov
Appl. Sci. 2021, 11(20), 9531; https://doi.org/10.3390/app11209531
Received: 12 August 2021 / Revised: 30 September 2021 / Accepted: 11 October 2021 / Published: 14 October 2021
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. View Full-Text
Keywords: fNIRS; machine learning; quality control fNIRS; machine learning; quality control
Show Figures

Figure 1

MDPI and ACS Style

Gabrieli, G.; Bizzego, A.; Neoh, M.J.Y.; Esposito, G. fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control. Appl. Sci. 2021, 11, 9531. https://doi.org/10.3390/app11209531

AMA Style

Gabrieli G, Bizzego A, Neoh MJY, Esposito G. fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control. Applied Sciences. 2021; 11(20):9531. https://doi.org/10.3390/app11209531

Chicago/Turabian Style

Gabrieli, Giulio, Andrea Bizzego, Michelle Jin Yee Neoh, and Gianluca Esposito. 2021. "fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control" Applied Sciences 11, no. 20: 9531. https://doi.org/10.3390/app11209531

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop