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

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...
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Gespeichert in:

Autor*in:	Robert eCooper [verfasserIn] Juliette eSelb [verfasserIn] Louis eGagnon [verfasserIn] Dorte ePhillip [verfasserIn] Henrik W Schytz [verfasserIn] Helle K Iversen [verfasserIn] Messoud eAshina [verfasserIn] David A Boas [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	NIRS near-infrared spectroscopy functional near-infrared spectroscopy hemodynamic response motion artifact

Übergeordnetes Werk:	In: Frontiers in Neuroscience - Frontiers Media S.A., 2008, 6(2012)
Übergeordnetes Werk:	volume:6 ; year:2012

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnins.2012.00147

Katalog-ID:	DOAJ015731731

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520			\|a 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 data sets 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. Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error and significant increase in the contrast-to-noise ratio 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 mean-squared error (55 %) while wavelet analysis produces the highest average increase in contrast-to-noise ratio (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.
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authorswithroles_txt_mv	Robert eCooper @@aut@@ Juliette eSelb @@aut@@ Louis eGagnon @@aut@@ Dorte ePhillip @@aut@@ Henrik W Schytz @@aut@@ Helle K Iversen @@aut@@ Messoud eAshina @@aut@@ David A Boas @@aut@@
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callnumber-first	R - Medicine
author	Robert eCooper
spellingShingle	Robert eCooper misc RC321-571 misc NIRS misc near-infrared spectroscopy misc functional near-infrared spectroscopy misc hemodynamic response misc motion artifact misc Neurosciences. Biological psychiatry. Neuropsychiatry A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy
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topic_title	RC321-571 A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy NIRS near-infrared spectroscopy functional near-infrared spectroscopy hemodynamic response motion artifact
topic	misc RC321-571 misc NIRS misc near-infrared spectroscopy misc functional near-infrared spectroscopy misc hemodynamic response misc motion artifact misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc NIRS misc near-infrared spectroscopy misc functional near-infrared spectroscopy misc hemodynamic response misc motion artifact misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy
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title_sort	systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy
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title_auth	A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy
abstract	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 data sets 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. Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error and significant increase in the contrast-to-noise ratio 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 mean-squared error (55 %) while wavelet analysis produces the highest average increase in contrast-to-noise ratio (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.
abstractGer	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 data sets 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. Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error and significant increase in the contrast-to-noise ratio 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 mean-squared error (55 %) while wavelet analysis produces the highest average increase in contrast-to-noise ratio (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.
abstract_unstemmed	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 data sets 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. Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error and significant increase in the contrast-to-noise ratio 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 mean-squared error (55 %) while wavelet analysis produces the highest average increase in contrast-to-noise ratio (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.
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title_short	A Systematic Comparison of Motion Artifact Correction Techniques for Functional Near-Infrared Spectroscopy
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