Marzetti L, Nolte G, Perrucci M G, Romani G L, Del Gratta C
Department of Clinical Sciences and Bioimaging, Gabriele D'Annunzio University, Italy.
Neuroimage. 2007 May 15;36(1):48-63. doi: 10.1016/j.neuroimage.2007.02.034. Epub 2007 Mar 3.
The study of large scale interactions in the brain from EEG signals is a promising method for the identification of functional networks. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference and the artifactual self-interactions between the measured EEG signals introduced by volume conduction. In this paper, we propose an approach to study large scale EEG coherency in which these factors are eliminated. Artifactual self-interaction by volume conduction is eliminated by using the imaginary part of the complex coherency as a measure of interaction and the Reference Electrode Standardization Technique (REST) is used for the approximate standardization of the reference of scalp EEG recordings to a point at infinity that, being far from all possible neural sources, acts like a neutral virtual reference. The application of our approach to simulated and real EEG data shows that the detection of interaction, as opposed to artifacts due to reference and volume conduction, is a goal that can be achieved from the study of a large scale parameter.
从脑电图（EEG）信号研究大脑中的大规模相互作用是识别功能网络的一种有前景的方法。然而，一个大规模参数的有效性受到两个因素的限制：使用非中性参考以及由容积传导引入的测量EEG信号之间的人为自相互作用。在本文中，我们提出了一种研究大规模EEG相干性的方法，其中这些因素被消除。通过使用复相干性的虚部作为相互作用的度量来消除由容积传导引起的人为自相互作用，并且参考电极标准化技术（REST）用于将头皮EEG记录的参考近似标准化到无穷远处的一个点，该点远离所有可能的神经源，起到中性虚拟参考的作用。我们的方法在模拟和真实EEG数据上的应用表明，与参考和容积传导引起的伪迹相反，相互作用的检测是一个可以通过研究大规模参数来实现的目标。
