Comparison of different cortical connectivity estimators for high-resolution EEG recordings
Comparison of different cortical connectivity estimators for high-resolution EEG recordings
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DOI：
10.1002/hbm.20263
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发表时间：
2007-02-01
影响因子：
4.8
通讯作者：
Babiloni, Fabio
中科院分区：
文献类型：
--
作者：
Astolfi, Laura;Cincotti, Febo;Babiloni, Fabio
这项工作的目的是定量地表征在频域中的技术体的性能，用于在实践中通常遇到的不同操作条件下从高分辨率EEG记录估计皮层连接。研究的连通性模式估计是定向传递函数（DTF），其修改称为直接DTF（dDTF）和部分定向相干（PDC）。预定义的模式的皮质连接进行了模拟，然后检索的应用程序的DTF，dDTF，和PDC的方法。信号噪声比（SNR）和长度（LENGTH）的EEG时期进行了研究，作为影响重建的强加的连接模式的因素。通过对重建后的连通性模式的质量进行评价，评价了重建质量和估计连通性模式的错误率。采用方差分析（ANOVA）对误差函数进行统计学分析。然后将整个方法应用于在著名的Stroop范式期间记录的高分辨率EEG数据。仿真结果表明，这三种方法都正确地估计了模拟的连接模式在合理的条件下。然而，性能的方法有所不同的SNR和长度因素的函数。当应用于Stroop数据时，这些方法通常是等效的。在一般情况下，可用EEG的量影响连接模式估计的准确性。对SNR为3或更高的27 s非连续记录的分析确保了可以准确地恢复连接模式，PDC的误差低于7%，DTF的误差低于5%。总之，功能连接模式的皮层活动，可以有效地估计在大多数EEG记录中满足的一般条件下，通过结合高分辨率EEG技术，线性逆估计的皮层活动，频域多变量方法，如PDC，DTF，和dDTF。
The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.