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Evaluating motion processing algorithms for use with functional near-infrared spectroscopy data from young children.

Lourdes M Delgado Reyes1, Kevin Bohache2, Sobanawartiny Wijeakumar3

  • 1University of East Anglia, School of Psychology, Norwich, United Kingdom.

Neurophotonics
|May 31, 2018
PubMed
Summary

Targeted principal component analysis (tPCA) effectively corrects motion artifacts in functional near-infrared spectroscopy (fNIRS) data from young children. This method proved more robust than other techniques, retaining more trials and providing stable hemodynamic responses.

Keywords:
child brain imagingfunctional near-infrared spectroscopymotion artifactmotion correction

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Motion artifacts significantly contaminate functional near-infrared spectroscopy (fNIRS) signals.
  • Existing motion correction methods (PCA, CBSI, wavelet, spline) have been evaluated with simulated data, but their efficacy in pediatric cognitive tasks is less understood.
  • fNIRS is frequently applied to infant and child populations, necessitating robust motion artifact correction for this demographic.

Purpose of the Study:

  • To evaluate and compare the effectiveness of motion correction algorithms within the HomER2 software for pediatric fNIRS data.
  • To quantitatively assess motion correction techniques using physiological metrics of the hemodynamic response.
  • To identify the most suitable motion correction method for fNIRS studies involving young children.

Main Methods:

  • Evaluation of motion correction algorithms including targeted principal component analysis (tPCA), spline interpolation, and correlation-based signal improvement (CBSI) using HomER2.
  • Quantitative comparison of techniques based on objective metrics of the hemodynamic response.
  • Head-to-head comparison of the most promising methods (tPCA, spline, CBSI) on pediatric fNIRS data.

Main Results:

  • Targeted PCA (tPCA), spline, and CBSI methods successfully retained a higher proportion of experimental trials compared to other approaches.
  • CBSI demonstrated artifact correction but occasionally resulted in unstable hemodynamic response functions (HRFs).
  • tPCA and spline methods exhibited robustness across all evaluation metrics, with tPCA consistently outperforming spline in direct comparisons.

Conclusions:

  • Targeted PCA (tPCA) is identified as a highly effective technique for mitigating motion artifacts in fNIRS data from young children.
  • Both tPCA and spline interpolation offer robust solutions for motion correction in pediatric fNIRS.
  • tPCA demonstrates superior performance over spline interpolation for motion artifact correction in this population.