A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy.
A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy.
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DOI：
10.3389/fnins.2012.00147
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发表时间：
2012
影响因子：
4.3
通讯作者：
Boas DA
中科院分区：
文献类型：
--
作者：
Cooper RJ;Selb J;Gagnon L;Phillip D;Schytz HW;Iversen HK;Ashina M;Boas DA
近红外光谱仪（NIRS）容易受到由NIRS光纤和头皮之间的相对运动引起的信号伪影的影响。这些伪影可能对功能性NIRS的实用性非常有害，特别是在运动可能不可避免的具有挑战性的受试者群体中。已经提出了许多从NIRS数据中去除运动伪影的方法。在本文中，我们系统地比较了各种已发表的近红外光谱运动校正技术的实用性，使用一个模拟的功能激活信号添加到20个真实的近红外光谱数据集，其中包含运动伪影。主成分分析，样条插值，小波分析，卡尔曼滤波方法进行了比较，彼此和标准的方法，使用的恢复，模拟血流动力学反应函数（HRF）的准确性。我们测试的四种运动校正技术中的每一种都产生了显着的均方误差（MSE）降低和显着增加的对比度噪声比（CNR）的恢复HRF相比，没有校正和相比，拒绝运动污染试验的过程。样条插值产生最大的平均减少MSE（55%），而小波分析产生最高的平均增加CNR（39%）。在此分析的基础上，我们建议常规应用运动校正技术（特别是样条插值或小波分析），以尽量减少运动伪影对功能性近红外光谱数据的影响。
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.
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影响因子：
5.7
作者：
Gagnon L;Perdue K;Greve DN;Goldenholz D;Kaskhedikar G;Boas DA
通讯作者：
Boas DA
影响因子：
5.7
作者：
Saager, Rolf B.;Telleri, Nicole L.;Berger, Andrew J.
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通讯作者：
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影响因子：
5.7
作者：
Wilcox, Teresa;Haslup, Jennifer A.;Boas, David A.
通讯作者：
Boas, David A.