Objective. Stereo-electroencephalography (SEEG) has recently gained importance in analyzing brain functions. Its high temporal resolution and spatial specificity make it a powerful tool to investigate the strength, direction, and spectral content of brain networks interactions, especially when these connections are stimulus-evoked. However, choosing the best approach to evaluate the flow of information is not trivial, due to the lack of validated methods explicitly designed for SEEG. Approach. We propose a novel non-parametric statistical test for event-related causality (ERC) assessment on SEEG recordings. Here, we refer to the ERC as the causality evoked by a particular part of the stimulus (a response window (RW)). We also present a data surrogation method to evaluate the performance of a causality estimation algorithm. We finally validated our pipeline using surrogate SEEG data derived from an experimentally collected dataset, and compared the most used and successful measures to estimate effective connectivity, belonging to the Geweke–Granger causality framework. Main results. Here we show that our workflow correctly identified all the directed connections in the RW of the surrogate data and prove the robustness of the procedure against synthetic noise with amplitude exceeding physiological-plausible values. Among the causality measures tested, partial directed coherence performed best. Significance. This is the first non-parametric statistical test for ERC estimation explicitly designed for SEEG datasets. The pipeline, in principle, can also be applied to the analysis of any type of time-varying estimator, if there exists a clearly defined RW.

客观的。立体脑电图 (SEEG) 最近在分析大脑功能方面变得越来越重要。其高时间分辨率和空间特异性使其成为研究大脑网络相互作用的强度、方向和光谱内容的有力工具，尤其是当这些连接是由刺激诱发时。然而，由于缺乏明确为 SEEG 设计的经过验证的方法，因此选择评估信息流的最佳方法并非易事。方法。我们提出了一种新的非参数统计检验，用于对 SEEG 记录进行事件相关因果关系 (ERC) 评估。在这里，我们将 ERC 称为由刺激的特定部分（响应窗口 (RW)）引起的因果关系。我们还提出了一种数据替代方法来评估因果关系估计算法的性能。我们最终使用源自实验收集的数据集的替代 SEEG 数据验证了我们的管道，并比较了最常用和最成功的措施来估计有效连接，属于 Geweke-Granger 因果关系框架。主要结果。在这里，我们展示了我们的工作流程正确识别了替代数据的 RW 中的所有定向连接，并证明了该过程对幅度超过生理合理值的合成噪声的稳健性。在测试的因果关系测量中，部分定向相干表现最好。意义。这是为 SEEG 数据集明确设计的 ERC 估计的第一个非参数统计检验。原则上，如果存在明确定义的 RW，管道也可以应用于任何类型的时变估计器的分析。