A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy

By Robert J. Cooper, Juliette Selb, Louis Gagnon, Dorte Phillip, H. Schytz, Helle Klingenberg Iversen, M. Ashina, David A. Boas. Frontiers in Neuroscience, 2012

409 citations recorded by the source corpus.

Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.

Subfields: Artificial Intelligence, Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/fce841984e482380464ed8d5d4619525d3a9e290

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Citations in the corpus

Motion artifacts in functional near-infrared spectroscopy: A comparison of motion correction techniques applied to real cognitive data (2014, methodology): “PCA run with 80% of the variance removed (PCA_80), instead, achieves the same pattern of results that Cooper et al. (2012) found in their paper: it is able to recover a significant number of trials and to significantly reduce AUC 0–2 and the standard deviation metrics compared to no motion…”
Statistical analysis of fNIRS data: A comprehensive review (2014, positive): “By using the simulated hemodynamic response function (HRF), Cooper et al. (2012) compared the performance of the four motion correction methods, including Kalman filtering (Izzetoglu et al., 2010), wavelet-based filtering (Molavi and Dumont, 2012), spline interpolation (Scholkmann et al., 2010),…”
Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS (2014, background): “A general overview of fNIRS artifacts and artifact removal techniques can be found in Cooper et al. (2012).”
fNIRS-based brain-computer interfaces: a review (2015, methodology): “…(i.e., principle component analysis (PCA)-based ﬁltering) (Zhang et al., 2005), wavelet-analysis-based methods (Sato et al., 2006; Power et al., 2010), Savitzky-Golay type ﬁlters (Hai et al., 2013; Shin and Jeong, 2014), and others (Cui et al., 2010a,b; Fekete et al., 2011; Cooper et al., 2012).”