The effect of filtering on Granger causality based multivariate causality measures
The effect of filtering on Granger causality based multivariate causality measures
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DOI：
10.1016/j.neuroimage.2009.12.050
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发表时间：
2010-04-01
期刊：
影响因子：
5.7
通讯作者：
Timmermann, Lars
中科院分区：
文献类型：
--
作者：
Florin, Esther;Gross, Joachim;Timmermann, Lars
在过去，基于格兰杰因果关系的因果度量已被建议用于评估神经信号的方向性。在神经数据的频域分析（功率或相干性）中，通常采用滤波或抽取的方法对时间序列进行预处理。然而，在其他领域，理论已经表明，与格兰杰因果关系相结合的过滤可能导致虚假或遗漏的因果关系。我们通过(a)模拟研究和(b)对脑磁图数据的应用，研究了这一结果是否可以转化为格兰杰因果关系衍生的多变量因果关系方法。为此，我们对应用不同过滤技术的效果进行了广泛的模拟，并评估了五种不同的多变量因果关系度量与两种数值显著性度量（随机排列和留一方法）相结合的性能。分析包括三种最广泛使用的滤波器（高通，低通，陷波滤波器），四种不同的滤波器类型（巴特沃斯，切比雪夫I和II，椭圆滤波器），滤波器阶数变化，抽取和插值。仿真结果表明，对待去除伪信号没有强先验的预处理会干扰数据的信息内容和时间顺序，导致虚假和遗漏的因果关系。只有当明显的伪影，如电流或运动伪影存在时，过滤掉相应的干扰似乎是可取的。虽然过采样不会造成问题，但如果抽取系数大于时间序列之间的最小时移，则可能导致错误的推断。通常，多变量因果度量对数据预处理非常敏感。(C) 2009爱思唯尔公司版权所有。
In the past, causality measures based on Granger causality have been suggested for assessing directionality in neural signals. In frequency domain analyses (power or coherence) of neural data, it is common to preprocess the time series by filtering or decimating. However, in other fields, it has been shown theoretically that filtering in combination with Granger causality may lead to spurious or missed causalities. We investigated whether this result translates to multivariate causality methods derived from Granger causality with (a) a simulation study and (b) an application to magnetoencephalographic data. To this end, we performed extensive simulations of the effect of applying different filtering techniques and evaluated the performance of five different multivariate causality measures in combination with two numerical significance measures (random permutation and leave one out method). The analysis included three of the most widely used filters (high-pass, low-pass, notch filter), four different filter types (Butterworth, Chebyshev I and II, elliptic filter), variation of filter order, decimating and interpolation.The simulation results suggest that preprocessing without a strong prior about the artifact to be removed disturbs the information content and time ordering of the data and leads to spurious and missed causalities. Only if apparent artifacts like a current or movement artifact are present, filtering out the respective disturbance seems advisable. While oversampling poses no problem, decimation by a factor greater than the minimum time shift between the time series may lead to wrong inferences. In general, the multivariate causality measures are very sensitive to data preprocessing. (C) 2009 Elsevier Inc. All rights reserved.