Learning based motion artifacts processing in fNIRS: a mini review

• Published in: Frontiers in neuroscience Vol. 17; p. 1280590
• Main Authors: Zhao, Yunyi, Luo, Haiming, Chen, Jianan, Loureiro, Rui, Yang, Shufan, Zhao, Hubin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 08.11.2023; Frontiers Media S.A
• Subjects: Biomedical engineering; Brain research; brain-computer interfaces; Data processing; Datasets; Deep learning; Discriminant analysis; Entropy; evaluation matrix; fNIRS; Hemodynamics; Hemoglobin; Infrared spectroscopy; Learning; Machine learning; Methods; motion artifacts; Neural networks; Neuroscience; Quality control; Signal processing; deep learning; fNIRS; brain-computer interfaces; motion artifacts; evaluation matrix; machine learning
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2023.1280590

This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA and compromise data integrity. Traditional strategies often result in reduced reliability of the hemodynamic response and statistical power. Recognizing the limited number of studies focusing on learning-based MA removal, we examine 315 studies, identifying seven pertinent to our focus area. We discuss the current landscape of learning-based MA correction methods and highlight research gaps. Noting the absence of standard evaluation metrics for quality assessment of MA correction, we suggest a novel framework, integrating signal and model quality considerations and employing metrics like ΔSignal-to-Noise Ratio (ΔSNR), confusion matrix, and Mean Squared Error. This work aims to facilitate the application of learning-based methodologies to fNIRS and improve the accuracy and reliability of neurovascular studies.