Learning based motion artifacts processing in fNIRS: a mini review.
brain-computer interfaces
deep learning
evaluation matrix
fNIRS
machine learning
motion artifacts
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2023
2023
Historique:
received:
20
08
2023
accepted:
11
10
2023
medline:
30
11
2023
pubmed:
30
11
2023
entrez:
30
11
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38033535
doi: 10.3389/fnins.2023.1280590
pmc: PMC10683641
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1280590Informations de copyright
Copyright © 2023 Zhao, Luo, Chen, Loureiro, Yang and Zhao.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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