Abstract
.SignificanceFunctional near-infrared spectroscopy (fNIRS) is a promising optical neuroimaging technique, measuring the hemodynamic signals from the cortex. However, improving signal quality and reducing artifacts arising from oscillation and baseline shift (BS) are still challenging up to now for fNIRS applications.AimConsidering the advantages and weaknesses of the different algorithms to reduce the artifact effect in fNIRS signals, we propose a hybrid artifact detection and correction approach.ApproachFirst, distinct artifact detection was realized through an fNIRS detection strategy. Then the artifacts were divided into three categories: BS, slight oscillation, and severe oscillation. A comprehensive correction was applied through three main steps: severe artifact correction by cubic spline interpolation, BS removal by spline interpolation, and slight oscillation reduction by dual-threshold wavelet-based method.ResultsUsing fNIRS data acquired during whole night sleep monitoring, we compared the performance of our approach with existing algorithms in signal-to-noise ratio (SNR) and Pearson’s correlation coefficient (). We found that the proposed method showed improvements in performance in SNR and with strong stability.ConclusionsThese results suggest that the new hybrid artifact detection and correction method enhances the viability of fNIRS as a functional neuroimaging modality.
Highlights
Localized neural activity is accompanied by hemodynamic oscillations caused by the metabolic activity and concurrent electrical activity
The motion artifacts are still problematic for functional near-infrared spectroscopy (fNIRS) monitoring, in longterm recordings with additional challenges to be addressed in comparison with the short-term experiments
The results showed spline interpolation produced the greatest improvement in mean-squared error, and wavelet analysis generated the greatest increase in contrast-to-noise ratio.[21]
Summary
Localized neural activity is accompanied by hemodynamic oscillations caused by the metabolic activity and concurrent electrical activity. The electrical firing requires more oxygenated blood supply to activate brain areas, causing an increase in local oxygenated hemoglobin and a decrease in deoxygenated hemoglobin. It results in an underlying link between electrical events. FNIRS technique is applied to long-term measurements, which are much more likely to exhibit frequent movement and produce motion artifacts in comparison with the short-term experiments. Rapid head shaking introduces great amplitude and high-frequency fluctuation in fNIRS signals, whereas slow head rotation will cause slow sustained varying oscillation along with lasting baseline shift (BS).[8] Failure to correct for the artifacts may lead to biased or spurious conclusions.[7] the motion artifacts are still problematic for fNIRS monitoring, in longterm recordings with additional challenges to be addressed in comparison with the short-term experiments. It is necessary to apply preprocessing approaches to eliminate the artifacts for fNIRS signal collection
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