ArticleNeurophotonics2022
Motion artifact correction for resting-state neonatal functional near-infrared spectroscopy through adaptive estimation of physiological oscillation denoising.
Article in Neurophotonics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Investigating brain haemodynamics during hypoglycaemia in very preterm neonates using diffuse optical tomography.medRxiv : the preprint server for health sciences · 2025Article
- Analysis of brain functional connectivity in children with autism spectrum disorder and sleep disorders: a fNIRS observational study.Frontiers in psychology · 2025Article
- Hemodynamic responses to emotional auditory stimuli in patients with prolonged disorders of consciousness: an fNIRS study.Frontiers in neurology · 2025Article
- Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data.Neurophotonics · 2024Article
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Authors and funding
7 authors.
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Abstract
Significance: Functional near-infrared spectroscopy (fNIRS) for resting-state neonatal brain function evaluation provides assistance for pediatricians in diagnosis and monitoring treatment outcomes. Artifact contamination is an important challenge in the application of fNIRS in the neonatal population. Aim: Our study aims to develop a correction algorithm that can effectively remove different types of artifacts from neonatal data. Approach: In the study, we estimate the recognition threshold based on the amplitude characteristics of the signal and artifacts. After artifact recognition, Spline and Gaussian replacements are used separately to correct the artifacts. Various correction method recovery effects on simulated artifact and actual neonatal data are compared using the Pearson correlation ( Results: The neonatal resting-state data corrected by our method showed better agreement with results by visual recognition and correction, and significant improvements ( Conclusions: The proposed algorithm corrects artifacts such as baseline shifts, spikes, and serial disturbances in neonatal fNIRS data quickly and more effectively. It can be used for preprocessing in clinical applications of neonatal fNIRS brain function detection.
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