title: Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data.
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.
authors: Department of Developmental Psychology, University of Padova, Italy. Electronic address: sabrina.brigadoi@studenti.unipd.it.
authors: Sabrina
authors: S
authors: Brigadoi
authors: Lisa
authors: L
authors: Ceccherini
authors: Simone
authors: S
authors: Cutini
authors: Fabio
authors: F
authors: Scarpa
authors: Pietro
authors: P
authors: Scatturin
authors: Juliette
authors: J
authors: Selb
authors: Louis
authors: L
authors: Gagnon
authors: David A
authors: DA
authors: Boas
authors: Robert J
authors: RJ
authors: Cooper
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D002560:Cerebrovascular Circulation / Q000502:physiology; D003627:Data Interpretation, Statistical; D005260:Female; D059907:Functional Neuroimaging / Q000379:methods*; D006439:Hemodynamics / Q000502:physiology; D006452:Hemoglobinometry; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007568:Jaw / Q000502:physiology; D008297:Male; D009038:Motion; D010100:Oxygen / Q000097:blood; D025341:Principal Component Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D058067:Wavelet Analysis; D055815:Young Adult
chemical_list: D010100:Oxygen
keywords: Functional near-infrared spectroscopy; Hemodynamic response; Motion artifact; Motion correction; fNIRS
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