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Barker JW, Rosso AL, Sparto PJ, et al. Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy. Neurophotonics. 2016;3(3):031410doi: 10.1117/1.NPh.3.3.031410.
Barker, J. W., Rosso, A. L., Sparto, P. J., & Huppert, T. J. (2016). Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy. Neurophotonics, 3(3), 031410. https://doi.org/10.1117/1.NPh.3.3.031410
Barker, Jeffrey W, et al. "Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy." Neurophotonics vol. 3,3 (2016): 031410. doi: https://doi.org/10.1117/1.NPh.3.3.031410
Barker JW, Rosso AL, Sparto PJ, Huppert TJ. Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy. Neurophotonics. 2016 Jul;3(3):031410. doi: 10.1117/1.NPh.3.3.031410. Epub 2016 May 23. PMID: 27226974; PMCID: PMC4876834.
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Neurophotonics. 2016 Jul;3(3):031410. doi: 10.1117/1.NPh.3.3.031410. Epub 2016 May 23.

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy.

Neurophotonics

Jeffrey W Barker, Andrea L Rosso, Patrick J Sparto, Theodore J Huppert

Affiliations

  1. University of Pittsburgh , Department of Radiology, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, United States.
  2. University of Pittsburgh , Department of Epidemiology, 130 De Soto Street, Pittsburgh, Pennsylvania 15261, United States.
  3. University of Pittsburgh , Department of Physical Therapy, Suite 210 Bridgeside Point, Pittsburgh, Pennsylvania 15213, United States.

PMID: 27226974 PMCID: PMC4876834 DOI: 10.1117/1.NPh.3.3.031410

Abstract

Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology. These two factors can reduce the sensitivity and specificity of detecting hemodynamic changes. In a previous work, we showed that these factors could be mitigated by autoregressive-based prewhitening followed by the application of an iterative reweighted least squares algorithm offline. This current work extends these same ideas to real-time analysis of brain signals by modifying the linear Kalman filter, resulting in an algorithm for online estimation that is robust to systemic physiology and motion artifacts. We evaluated the performance of the proposed method via simulations of evoked hemodynamics that were added to experimental resting-state data, which provided realistic fNIRS noise. Last, we applied the method post hoc to data from a standing balance task. Overall, the new method showed good agreement with the analogous offline algorithm, in which both methods outperformed ordinary least squares methods.

Keywords: Kalman filter; functional near-infrared spectroscopy; motion artifacts; real-time analysis; serial correlations

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