Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163047
Title: A machine learning perspective on fNIRS signal quality control approaches
Authors: Bizzego, Andrea
Neoh, Michelle
Gabrieli, Giulio
Esposito, Gianluca
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Social sciences::Psychology
Issue Date: 2022
Source: Bizzego, A., Neoh, M., Gabrieli, G. & Esposito, G. (2022). A machine learning perspective on fNIRS signal quality control approaches. IEEE Transactions On Neural Systems and Rehabilitation Engineering, 30, 2292-2300. https://dx.doi.org/10.1109/TNSRE.2022.3198110
Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering 
Abstract: Despite a rise in the use of functional Near Infra-Red Spectroscopy (fNIRS) to study neural systems, fNIRS signal processing is not standardized and is highly affected by empirical and manual procedures. At the beginning of any signal processing procedure, Signal Quality Control (SQC) is critical to prevent errors and unreliable results. In fNIRS analysis, SQC currently relies on applying empirical thresholds to handcrafted Signal Quality Indicators (SQIs). In this study, we use a dataset of fNIRS signals (N = 1,340) recorded from 67 subjects, and manually label the signal quality of a subset of segments (N = 548) to investigate the pitfalls of current practices while exploring the opportunities provided by Deep Learning approaches. We show that SQIs statistically discriminate signals with bad quality, but the identification by means of empirical thresholds lacks sensitivity. Alternatively to manual thresholding, conventional machine learning models based on the SQIs have been proven more accurate, with end-to-end approaches, based on Convolutional Neural Networks, capable of further improving the performance. The proposed approach, based on machine learning, represents a more objective SQC for fNIRS and moves towards the use of fully automated and standardized procedures.
URI: https://hdl.handle.net/10356/163047
ISSN: 1534-4320
DOI: 10.1109/TNSRE.2022.3198110
Rights: © The authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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