Laboratory for Intelligent Imaging and Neural Computing, Columbia University, New York, NY, 10027, USA.
Machine Learning Department, Technische Universität Berlin, 10587, Berlin, Germany.
Brain Topogr. 2019 Jul;32(4):625-642. doi: 10.1007/s10548-016-0498-y. Epub 2016 Jun 2.
Due to its high temporal resolution, electroencephalography (EEG) is widely used to study functional and effective brain connectivity. Yet, there is currently a mismatch between the vastness of studies conducted and the degree to which the employed analyses are theoretically understood and empirically validated. We here provide a simulation framework that enables researchers to test their analysis pipelines on realistic pseudo-EEG data. We construct a minimal example of brain interaction, which we propose as a benchmark for assessing a methodology's general eligibility for EEG-based connectivity estimation. We envision that this benchmark be extended in a collaborative effort to validate methods in more complex scenarios. Quantitative metrics are defined to assess a method's performance in terms of source localization, connectivity detection and directionality estimation. All data and code needed for generating pseudo-EEG data, conducting source reconstruction and connectivity estimation using baseline methods from the literature, evaluating performance metrics, as well as plotting results, are made publicly available. While this article covers only EEG modeling, we will also provide a magnetoencephalography version of our framework online.
由于其高时间分辨率，脑电图 (EEG) 被广泛用于研究功能和有效的大脑连接。然而，目前进行的研究数量之庞大与所采用的分析在理论上的理解和经验上的验证程度之间存在不匹配。我们在这里提供了一个模拟框架，使研究人员能够在真实的伪 EEG 数据上测试他们的分析管道。我们构建了一个大脑交互的最小示例，我们将其作为评估方法在 EEG 连接估计方面的一般适用性的基准。我们设想这个基准可以通过合作扩展，以验证更复杂情况下的方法。定义了定量指标，以根据源定位、连接检测和方向性估计来评估方法的性能。生成伪 EEG 数据、使用文献中的基线方法进行源重建和连接估计、评估性能指标以及绘制结果所需的所有数据和代码都已公开。虽然本文仅涵盖 EEG 建模，但我们也将在线提供我们框架的脑磁图版本。
