Key result
A novel physiological de-noising method based on wavelet coherence analysis identified three main noise processes (respiration, Mayer waves, and local skin blood flow regulation) and significantly improved the sensitivity of fNIRS to cerebral signals.
Population
15 healthy subjects. Data from one subject was excluded, leaving n=14 for analysis.
Design
Other
Authors
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May enhance fNIRS signal quality in research; leaves open prospective validation before clinical use.
A novel physiological de-noising method using wavelet coherence analysis and concurrent peripheral physiology recordings improves the sensitivity of fNIRS to cerebral signals by removing noise from respiration, Mayer waves, and local skin blood flow.
Kirlilna et al. (2013) studied Healthy (n=15). Physiological de-noising method using wavelet coherence analysis and GLM vs. Standard fNIRS analysis without physiological de-noising was evaluated on Identification of physiological noise components and improvement of fNIRS sensitivity to cerebral signals. A novel physiological de-noising method based on wavelet coherence analysis identified three main noise processes (respiration, Mayer waves, and local skin blood flow regulation) and significantly improved the sensitivity of fNIRS to cerebral signals.
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