Abstract
Motion artifacts are a significant source of noise in many functional near-infrared spectroscopy (fNIRS) experiments. Despite this, there is no well-established method for their removal. Instead, functional trials of fNIRS data containing a motion artifact are often rejected completely. However, in most experimental circumstances the number of trials is limited, and multiple motion artifacts are common, particularly in challenging populations. Many methods have been proposed recently to correct for motion artifacts, including principle component analysis, spline interpolation, Kalman filtering, wavelet filtering and correlation-based signal improvement. The performance of different techniques has been often compared in simulations, but only rarely has it been assessed on real functional data. Here, we compare the performance of these motion correction techniques on real functional data acquired during a cognitive task, which required the participant to speak aloud, leading to a low-frequency, low-amplitude motion artifact that is correlated with the hemodynamic response. To compare the efficacy of these methods, objective metrics related to the physiology of the hemodynamic response have been derived. Our results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases. Our results therefore support previous studies that have shown wavelet filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data. The analyses performed here can serve as a guide for others to objectively test the impact of different motion correction algorithms and therefore select the most appropriate for the analysis of their own fNIRS experiment.
Highlights
Functional near-infrared spectroscopy is a non-invasive neuroimaging technique, which uses light in the near-infrared range to infer cerebral activity
This approach is only suitable if the number of motion artifacts detected is low and the number of trials is high, otherwise the risk is that too few trials will be accepted, resulting in a very noisy hemodynamic response. Functional near-infrared spectroscopy (fNIRS) is suited for examining challenging populations who might not be investigated with fMRI
Motion artifact correction is an essential step in the fNIRS data processing pipeline
Summary
Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique, which uses light in the near-infrared range to infer cerebral activity. A common and simple way to solve the issue of motion artifacts is to reject all trials where a motion artifact has been detected This approach is only suitable if the number of motion artifacts detected is low and the number of trials is high, otherwise the risk is that too few trials will be accepted, resulting in a very noisy hemodynamic response. FNIRS is suited for examining challenging populations (e.g. infants, clinical patients, children) who might not be investigated with fMRI In these populations the number of functional trials is almost always strictly limited, and trial rejection might not be feasible
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