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Journal of Biomedical Optics

Empirical mode decomposition-based motion artifact correction method for functional near-infrared spectroscopy
Author(s): Yue Gu; Junxia Han; Zhenhu Liang; Jiaqing Yan; Zheng Li; Xiaoli Li
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Paper Abstract

Functional near-infrared spectroscopy (fNIRS) is a promising technique for monitoring brain activity. However, it is sensitive to motion artifacts. Many methods have been developed for motion correction, such as spline interpolation, wavelet filtering, and kurtosis-based wavelet filtering. We propose a motion correction method based on empirical mode decomposition (EMD), which is applied to segments of data identified as having motion artifacts. The EMD method is adaptive, data-driven, and well suited for nonstationary data. To test the performance of the proposed EMD method and to compare it with other motion correction methods, we used simulated hemodynamic responses added to real resting-state fNIRS data. The EMD method reduced mean squared error in 79% of channels and increased signal-to-noise ratio in 78% of channels. Moreover, it produced the highest Pearson’s correlation coefficient between the recovered signal and the original signal, significantly better than the comparison methods (p<0.01, paired t-test). These results indicate that the proposed EMD method is a first choice method for motion artifact correction in fNIRS.

Paper Details

Date Published: 6 January 2016
PDF: 9 pages
J. Biomed. Opt. 21(1) 015002 doi: 10.1117/1.JBO.21.1.015002
Published in: Journal of Biomedical Optics Volume 21, Issue 1
Show Author Affiliations
Yue Gu, Yanshan Univ. (China)
Junxia Han, Beijing Normal Univ. (China)
(China)
Zhenhu Liang, Yanshan Univ. (China)
Jiaqing Yan, Yanshan Univ. (China)
Zheng Li, Beijing Normal Univ. (China)
(China)
Xiaoli Li, Beijing Normal Univ. (China)
(China)


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