ORCID
- Giulio Gabrieli: 0000-0002-9846-5767
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.
Keywords
Deep learning, functional near infrared spectroscopy, machine learning, signal quality control
DOI Link
Publication Date
2022-01-01
Publication Title
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume
30
ISSN
1534-4320
First Page
2292
Last Page
2300
Deposit Date
2026-07-07
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Bizzego, Andrea; Neoh, Michelle; Gabrieli, Giulio; and Esposito, Gianluca, "A Machine Learning Perspective on fNIRS Signal Quality Control Approaches" (2022). Research Outputs: 2025-Present. 6.
https://arrow.tudublin.ie/libraryro/6