A novel robust Student's t-based Granger causality for EEG based brain network analysis
A novel robust Student's t-based Granger causality for EEG based brain network analysis
复制标题
一种新颖的基于学生的稳健格兰杰因果关系，用于基于脑电图的脑网络分析
DOI：
10.1016/j.bspc.2022.104321
复制
发表时间：
2022-10-26
影响因子：
5.1
通讯作者：
Li, Peiyang
中科院分区：
文献类型：
--
作者：
Gao, Xiaohui;Huang, Weijie;Li, Peiyang
基于Granger因果关系的脑网络分析已被广泛应用于基于脑电的神经科学研究和临床诊断，如运动想象情绪分析和癫痫发作预测。然而，由于眼部伪影等不可避免的离群点的影响，如何准确估计多个脑区之间的因果相互作用，并可靠地揭示潜在的神经机制，仍然是一个巨大的挑战，这可能导致网络估计的偏差和固有认知状态的解码失败。本文将学生t分布引入多元自回归(MVAR)模型，提出了一种新的Granger因果分析方法来抑制有向脑网络分析中离群点的影响。为了定量评估我们提出的方法的性能，我们进行了仿真研究和运动想象脑电信号实验。通过这两个定量实验，验证了我们提出的方法在捕获固有网络模式时对孤立点影响的稳健性。基于其稳健性，我们将其应用于情绪的脑电分析，评估了其在为情绪识别提供区分网络结构方面的有效性，并发现了不同情绪状态的生物标志物。这些生物标志物进一步揭示了当男性和女性受试者经历不同的情绪状态时，他们之间的网络拓扑差异。总的来说，我们进行的实验结果一致地证明了我们提出的方法在复杂伪影条件下的定向脑网络分析的稳健性和有效性，这可能为基于网络的神经认知研究提供可靠的证据。
Granger-causality-based brain network analysis has been widely applied in EEG-based neuroscience researches and clinical diagnoses, such as motor imagery emotion analysis and seizure prediction. However, how to accurately estimate the causal interactions among multiple brain regions and reveal potential neural mechanisms in a reliable way is still a great challenge, due to the influence of inevitable outliers such as ocular artifacts, which may lead to the deviation of network estimation and the decoding failure of the inherent cognitive states. In this work, by introducing Student's t-distribution into multivariate autoregressive (MVAR) model, we pro-posed a novel Granger causality analysis to suppress the outliers influence in directed brain network analysis. To quantitatively evaluate the performance of our proposed method, both simulation study and motor imagery EEG experiment were conducted. Through these two quantitative experiments, we verified the robustness of our proposed method to outlier influence when applying it to capture the inherent network patterns. Based on its robustness, we applied it for EEG analysis of emotions and assessed its efficiency in offering discriminative network structures for emotion recognition and discovered the biomarkers for different emotional states. These biomarkers further revealed the network-topology differences between male and female subjects when they experienced different emotional states. In general, our conducted experimental results consistently proved the robustness and efficiency of our proposed method for directed brain network analysis under complex artifact conditions, which may offer reliable evidence for network-based neurocognitive research.