A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data.
A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data.
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一种基于峰度的小波算法用于近红外光谱（fNIRS）数据的运动伪影校正
DOI：
10.1016/j.neuroimage.2015.02.057
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发表时间：
2015-05-15
期刊：
影响因子：
5.7
通讯作者：
Gratton G
中科院分区：
文献类型：
--
作者：
Chiarelli AM;Maclin EL;Fabiani M;Gratton G
运动是功能性近红外光谱（fNIRS）中伪影的主要来源。已经开发了几种用于fNIRS数据的运动伪影校正的算法，包括主成分分析（PCA）、目标主成分分析（tPCA）、样条插值（SI）和小波滤波（WF）。WF是基于去除小波系数被认为是基于他们的标准化分数离群值，它已被证明是有效的合成和真实的数据。然而，当SNR高时，它可能导致信号幅度的降低。这可能发生，因为标准化分数固有地适应于噪声水平，而与小波系数的分布形状无关。小波系数分布的高阶矩比其方差更能提供小波分布异常的诊断指标。在这里，我们介绍了一种新的过程，该过程依赖于消除有助于生成大的四阶矩的小波（即，峰度）来定义“离群值”小波（基于峰度的小波滤波，kbWF）。我们通过将其与其他现有程序进行比较来测试kbWF，使用添加到真实的静息状态fNIRS记录中的模拟功能性血流动力学反应。这些模拟表明，kbWF在消除瞬态噪声方面非常有效，在很宽的信号和噪声幅度范围内产生比其他现有方法更高SNR的结果。这是因为：（1）该过程是迭代的;（2）在识别离群值时，峰度比方差更具诊断性。然而，kbWF并不能消除持续时间与总记录时间相当的伪影的慢分量。
Movements are a major source of artifacts in functional Near-Infrared Spectroscopy (fNIRS). Several algorithms have been developed for motion artifact correction of fNIRS data, including Principal Component Analysis (PCA), targeted Principal Component Analysis (tPCA), Spline Interpolation (SI), and Wavelet Filtering (WF). WF is based on removing wavelets with coefficients deemed to be outliers based on their standardized scores, and it has proven to be effective on both synthetized and real data. However, when the SNR is high, it can lead to a reduction of signal amplitude. This may occur because standardized scores inherently adapt to the noise level, independently of the shape of the distribution of the wavelet coefficients. Higher-order moments of the wavelet coefficient distribution may provide a more diagnostic index of wavelet distribution abnormality than its variance. Here we introduce a new procedure that relies on eliminating wavelets that contribute to generate a large fourth-moment (i.e., kurtosis) of the coefficient distribution to define “outliers” wavelets (kurtosis-based Wavelet Filtering, kbWF). We tested kbWF by comparing it with other existing procedures, using simulated functional hemodynamic responses added to real resting-state fNIRS recordings. These simulations show that kbWF is highly effective in eliminating transient noise, yielding results with higher SNR than other existing methods over a wide range of signal and noise amplitudes. This is because: (1) the procedure is iterative; and (2) kurtosis is more diagnostic than variance in identifying outliers. However, kbWF does not eliminate slow components of artifacts whose duration is comparable to the total recording time.
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影响因子：
2.3
作者：
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DOI：
10.1098/rspb.2008.0986
发表时间：
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期刊：
Proceedings. Biological sciences
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作者：
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通讯作者：
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DOI：
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发表时间：
1997-07-01
期刊：
COGNITIVE BRAIN RESEARCH
影响因子：
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作者：
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通讯作者：
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