Rotman Research Institute of Baycrest, Canada.
Neuroimage. 2010 Jan 15;49(2):1593-600. doi: 10.1016/j.neuroimage.2009.08.027. Epub 2009 Aug 18.
This paper presents a data-driven pipeline for studying asymmetries in mutual interdependencies between distinct components of EEG signal. Due to volume conductance, estimating coherence between scalp electrodes may lead to spurious results. A group-based independent component analysis (ICA), which is conducted across all subjects and conditions simultaneously, is an alternative representation of the EEG measurements. Within this approach, the extracted components are independent in a global sense while short-lived or transient interdependencies may still be present between the components. In this paper, functional roles of the ICA components are specified through a partial least squares (PLS) analysis of task effects within the time course of the derived components. Functional integration is estimated within the information-theoretic approach using transfer entropy analysis based on asymmetries in mutual interdependencies of reconstructed phase dynamics. A secondary PLS analysis is performed to assess robust task-specific changes in transfer entropy estimates between functionally specific components.
本文提出了一种数据驱动的管道，用于研究 EEG 信号不同成分之间相互依存关系的不对称性。由于容积传导，估计头皮电极之间的相干性可能会导致虚假结果。基于群组的独立成分分析（ICA）是 EEG 测量的另一种表示形式，它可以同时在所有受试者和条件下进行。在这种方法中，提取的组件在全局意义上是独立的，而组件之间可能仍然存在短暂或瞬态的相互依存关系。在本文中，通过对衍生组件的时间过程中的任务效应进行偏最小二乘（PLS）分析，指定 ICA 组件的功能角色。使用基于重建相位动力学相互依存关系不对称的转移熵分析，在信息论方法中估计功能整合。执行二次 PLS 分析以评估功能特定组件之间转移熵估计值的稳健任务特定变化。
