Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement.

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

SUBMITTER: Al-Omairi HR 

PROVIDER: S-EPMC10146128 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature