IEEE Trans Neural Netw Learn Syst. 2024 Nov;35(11):16181-16195. doi: 10.1109/TNNLS.2023.3292179. Epub 2024 Oct 29.
Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.
基于格兰杰因果关系的有效脑连接提供了一种强大的工具，可以探究信息处理的神经机制和脑机接口的潜在特征。然而，在实际应用中，传统的格兰杰因果关系容易受到离群值的影响，例如不可避免的眼动伪迹，导致不合理的脑连接和无法破译内在认知状态。在这项工作中，受在强生理离群噪声条件下构建稀疏因果脑网络的启发，我们提出了一种双拉普拉斯格兰杰因果分析（DLap-GCA），通过在模型参数和残差上施加拉普拉斯分布。本质上，残差上的第一个拉普拉斯假设将抵抗脑电图（EEG）中离群值对因果推断的影响，而模型参数上的第二个拉普拉斯假设将稀疏地刻画多个脑区之间的内在相互作用。通过模拟研究，我们定量验证了它在抑制复杂离群值影响、模型估计的稳定能力和稀疏网络推断方面的有效性。对运动想象（MI）EEG 的应用进一步表明，即使在强噪声条件下，我们的方法也可以有效地捕捉 MI 任务的内在半球侧化，具有稀疏模式。基于所提出方法得出的网络特征的 MI 分类显示出比其他现有传统方法更高的准确性，这归因于 DLap-GCA 甚至在单次试验在线条件下也能及时捕捉到有区别的网络结构。基本上，这些结果一致表明它对复杂离群值影响的鲁棒性和对认知信息处理有代表性的脑网络进行特征刻画的能力，这为认知研究和未来脑机接口（BCI）的实现提供了可靠的网络结构的潜力。
