有限数据长度对频域连接性度量鲁棒性的比较研究
A comparative study of the robustness of frequency--domain connectivity measures to finite data length
AI总结：
本文通过数值模拟研究数据时间长度对EEG时间序列脑连接估计可靠性的影响，比较了IC、gPDC和fGC三种连接性度量在小样本下的鲁棒性差异。
摘要：
本文通过数值模拟研究了数据时间长度对脑连接估计可靠性的影響。我们假设神经源遵循稳定的多变量自回归模型，并考虑三种连接性度量：Imaginary part of Coherency (IC)、generalized Partial Directed Coherence (gPDC) 和 frequency-domain Granger Causality (fGC)。为了评估估计值的统计显著性，我们使用相位随机化和自回归的替代数据进行检验。首先考虑理想情况，即已知源时间课程。我们展示了即使确切知道源时间课程，当样本数变小时，连接性估计也不可靠；然而，尽管gPDC和fGC倾向于产生更多假阳性，IC对连接性的存在不敏感。接着我们考虑更现实的模拟，其中源时间课程通过eLORETA估计，EEG信号受生物噪声影响。以理想情况为参考，我们展示生物噪声对IC估计的影响与对gPDC和fGC的影响在定性上不同。
英文摘要：
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.
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