Original Research ARTICLE

Front. Neurosci., 11 October 2012 | doi: 10.3389/fnins.2012.00147

A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy

  • 1Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
  • 2Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA
  • 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 4Danish Headache Center, Department of Neurology, Glostrup Hospital, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
  • 5Stroke Unit, Department of Neurology, Glostrup Hospital, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

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.

Keywords: near-infrared spectroscopy, functional near-infrared spectroscopy, NIRS, motion artifact, hemodynamic response

Citation: Cooper RJ, Selb J, Gagnon L, Phillip D, Schytz HW, Iversen HK, Ashina M and Boas DA (2012) A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Front. Neurosci. 6:147. doi: 10.3389/fnins.2012.00147

Received: 25 July 2012; Accepted: 17 September 2012;
Published online: 11 October 2012.

Edited by:

Jessica A. Turner, Mind Research Network, USA

Reviewed by:

Joshua Vogelstein, Johns Hopkins, USA
Felix Scholkmann, Biomedical Optics Research Laboratory, Switzerland

Copyright: © 2012 Cooper, Selb, Gagnon, Phillip, Schytz, Iversen, Ashina and Boas. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.

*Correspondence: David A. Boas, Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Room 2301, 149, 13th Street, Charlestown, MA 02129, USA. e-mail: dboas@nmr.mgh.harvard.edu

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