A machine learning perspective on fNIRS signal quality control approaches

• Main Authors: Bizzego, Andrea, Neoh, Michelle, Gabrieli, Giulio, Esposito, Gianluca
• Other Authors: School of Social Sciences
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Social sciences::Psychology; Deep Learning; fNIRS; Machine Learning; Signal Quality Control
• Online Access: https://hdl.handle.net/10356/163047
• Institution: Nanyang Technological University

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...