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Journal of Biomedical Optics

Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children
Author(s): Xiao-Su Hu; Maria M. Arredondo; Megan Gomba; Nicole Confer; Alexandre F. DaSilva; Timothy D. Johnson; Mark Shalinsky; Ioulia Kovelman
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Paper Abstract

Motion artifacts are the most significant sources of noise in the context of pediatric brain imaging designs and data analyses, especially in applications of functional near-infrared spectroscopy (fNIRS), in which it can completely affect the quality of the data acquired. Different methods have been developed to correct motion artifacts in fNIRS data, but the relative effectiveness of these methods for data from child and infant subjects (which is often found to be significantly noisier than adult data) remains largely unexplored. The issue is further complicated by the heterogeneity of fNIRS data artifacts. We compared the efficacy of the six most prevalent motion artifact correction techniques with fNIRS data acquired from children participating in a language acquisition task, including wavelet, spline interpolation, principal component analysis, moving average (MA), correlation-based signal improvement, and combination of wavelet and MA. The evaluation of five predefined metrics suggests that the MA and wavelet methods yield the best outcomes. These findings elucidate the varied nature of fNIRS data artifacts and the efficacy of artifact correction methods with pediatric populations, as well as help inform both the theory and practice of optical brain imaging analysis.

Paper Details

Date Published: 11 December 2015
PDF: 9 pages
J. Biomed. Opt. 20(12) 126003 doi: 10.1117/1.JBO.20.12.126003
Published in: Journal of Biomedical Optics Volume 20, Issue 12
Show Author Affiliations
Xiao-Su Hu, Univ. of Michigan (United States)
Maria M. Arredondo, Univ. of Michigan (United States)
Megan Gomba, Univ. of Michigan (United States)
Nicole Confer, Univ. of Michigan (United States)
Alexandre F. DaSilva, Univ. of Michigan (United States)
Timothy D. Johnson, Univ. of Michigan (United States)
Mark Shalinsky, Univ. of Michigan (United States)
Ioulia Kovelman, Univ. of Michigan (United States)


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