ArticleSensors (Basel, Switzerland)2023
Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Spatiotemporal disruption of prefrontal dynamics during affective association in depression: an fNIRS case-control study.BMC psychiatry · 2026Article
- Impact of AI-generated characters on visual attention and prefrontal cognitive responses across age and image type conditions: using fNIRS and eye-tracking.Frontiers in human neuroscience · 2026Article
- Decreased brain activity within the dorsolateral prefrontal cortex in adolescents during cognitive control tasks: a functional near-infrared spectroscopy study.Neurophotonics · 2026Article
- Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.Neurophotonics · 2024Article
- Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique.Sensors (Basel, Switzerland) · 2024Article
- Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Functional near-infrared spectroscopy (fNIRS) is an optical non-invasive neuroimaging technique that allows participants to move relatively freely. However, head movements frequently cause optode movements relative to the head, leading to motion artifacts (MA) in the measured signal. Here, we propose an improved algorithmic approach for MA correction that combines wavelet and correlation-based signal improvement (WCBSI). We compare its MA correction accuracy to multiple established correction approaches (spline interpolation, spline-Savitzky-Golay filter, principal component analysis, targeted principal component analysis, robust locally weighted regression smoothing filter, wavelet filter, and correlation-based signal improvement) on real data. Therefore, we measured brain activity in 20 participants performing a hand-tapping task and simultaneously moving their head to produce MAs at different levels of severity. In order to obtain a "ground truth" brain activation, we added a condition in which only the tapping task was performed. We compared the MA correction performance among the algorithms on four predefined metrics (R,
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