标题： 频域连通性度量对有限数据长度的鲁棒性比较研究 显示英文标题

标题： A comparative study of the robustness of frequency--domain connectivity measures to finite data length

摘要： 在本工作中，我们使用数值模拟来研究数据的时间长度如何影响从脑电图时间序列中估计脑连接性的可靠性。 我们假设神经源遵循一个稳定的多变量自回归模型，并考虑三种连接度量：相干性的虚部（IC）、广义部分定向相干性（gPDC）和频域格兰杰因果关系（fGC）。 为了评估估计值的统计显著性，我们通过生成相位随机化和自回归的替代数据来进行替代数据检验。 我们首先考虑理想情况，在这种情况下，我们确切地知道源时间过程。 在这里，我们展示了即使确切地知道源时间过程，在样本数量较小时也不足以提供可靠的连接性估计；然而，虽然gPDC和fGC倾向于提供更多的假阳性结果，IC对连接性的存在则不太敏感。 然后我们进行更现实的模拟，在这些模拟中，源时间过程是通过eLORETA估计的，并且脑电图信号受到强度不断增加的生物噪声的影响。 以理想情况为参考，我们展示了生物噪声对IC估计的影响与对gPDC和fGC的影响在定性上是不同的。 显示英文摘要

摘要： In this work we use numerical simulation to investigate how the temporal length of the data affects the reliability of the estimates of brain connectivity from EEG time--series. We assume that the neural sources follow a stable MultiVariate AutoRegressive model, and consider three connectivity metrics: Imaginary part of Coherency (IC), generalized Partial Directed Coherence (gPDC) and frequency--domain Granger Causality (fGC). In order to assess the statistical significance of the estimated values, we use the surrogate data test by generating phase--randomized and autoregressive surrogate data. We first consider the ideal case where we know the source time courses exactly. Here we show how, expectedly, even exact knowledge of the source time courses is not sufficient to provide reliable estimates of the connectivity when the number of samples gets small; however, while gPDC and fGC tend to provide a larger number of false positives, the IC becomes less sensitive to the presence of connectivity. Then we proceed with more realistic simulations, where the source time courses are estimated using eLORETA, and the EEG signal is affected by biological noise of increasing intensity. Using the ideal case as a reference, we show that the impact of biological noise on IC estimates is qualitatively different from the impact on gPDC and fGC.