Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data.
Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data.
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DOI：
10.1016/j.neuroimage.2013.04.082
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发表时间：
2014-01-15
期刊：
影响因子：
5.7
通讯作者：
Cooper, Robert J.
中科院分区：
文献类型：
--
作者：
Brigadoi, Sabrina;Ceccherini, Lisa;Cutini, Simone;Scarpa, Fabio;Scatturin, Pietro;Selb, Juliette;Gagnon, Louis;Boas, David A.;Cooper, Robert J.
运动伪影是许多功能性近红外光谱 (fNIRS) 实验中的重要噪声源。尽管如此，还没有成熟的方法来去除它们。相反，包含运动伪影的 fNIRS 数据的功能试验通常会被完全拒绝。然而，在大多数实验环境中，试验次数是有限的，并且多个运动伪影很常见，特别是在具有挑战性的人群中。最近提出了许多方法来校正运动伪影，包括主成分分析、样条插值、卡尔曼滤波、小波滤波和基于相关的信号改进。人们经常在模拟中比较不同技术的性能，但很少在实际功能数据上进行评估。在这里，我们比较了这些运动校正技术在认知任务期间获取的真实功能数据的性能，该任务要求参与者大声说话，从而导致与血流动力学响应相关的低频、低幅度运动伪影。为了比较这些方法的功效，得出了与血流动力学反应生理学相关的客观指标。我们的结果表明，校正运动伪影总是比拒绝试验更好，并且小波滤波是校正此类伪影的最有效方法，可以减少 93% 的情况下存在伪影的曲线下面积。因此，我们的结果支持了先前的研究，这些研究表明小波滤波是校正 fNIRS 数据中运动伪影的最有前途和最强大的技术。这里进行的分析可以作为其他人客观测试不同运动校正算法的影响的指南，从而选择最合适的算法来分析他们自己的 fNIRS 实验。
Motion artifacts are a significant source of noise in many functional near-infrared spectroscopy (fNIRS) experiments. Despite this, there is no well-established method for their removal. Instead, functional trials of fNIRS data containing a motion artifact are often rejected completely. However, in most experimental circumstances the number of trials is limited, and multiple motion artifacts are common, particularly in challenging populations. Many methods have been proposed recently to correct for motion artifacts, including principle component analysis, spline interpolation, Kalman filtering, wavelet filtering and correlation-based signal improvement. The performance of different techniques has been often compared in simulations, but only rarely has it been assessed on real functional data. Here, we compare the performance of these motion correction techniques on real functional data acquired during a cognitive task, which required the participant to speak aloud, leading to a low-frequency, low-amplitude motion artifact that is correlated with the hemodynamic response. To compare the efficacy of these methods, objective metrics related to the physiology of the hemodynamic response have been derived. Our results show that it is always better to correct for motion artifacts than reject trials, and that wavelet filtering is the most effective approach to correcting this type of artifact, reducing the area under the curve where the artifact is present in 93% of the cases. Our results therefore support previous studies that have shown wavelet filtering to be the most promising and powerful technique for the correction of motion artifacts in fNIRS data. The analyses performed here can serve as a guide for others to objectively test the impact of different motion correction algorithms and therefore select the most appropriate for the analysis of their own fNIRS experiment.
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影响因子：
3
作者：
Koechel, Angelika;Plichta, Michael M.;Schienle, Anne
通讯作者：
Schienle, Anne
影响因子：
5.7
作者：
Plichta, M. M.;Heinzel, S.;Fallgatter, A. J.
通讯作者：
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影响因子：
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作者：
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影响因子：
3.5
作者：
Muehlschlegel, Susanne;Selb, J.;Patel, M.;Diamond, S. G.;Franceschini, M. A.;Sorensen, A. G.;Boas, D. A.;Schwamm, L. H.
通讯作者：
Schwamm, L. H.
影响因子：
5.7
作者：
Huppert, TJ;Hoge, RD;Boas, DA
通讯作者：
Boas, DA