Sparse time series models have shown promise in estimating contemporaneous and ongoing brain connectivity. This paper was motivated by a neuroscience experiment using EEG signals as the outcome of our established interventional protocol, a new method in neurorehabilitation toward developing a treatment for visual verticality disorder in post-stroke patients. To analyze the [complex outcome measure (EEG)] that reflects neural-network functioning and processing in more specific ways regarding traditional analyses, we make a comparison among sparse time series models (classic VAR, GLASSO, TSCGM, and TSCGM-modified with non-linear and iterative optimizations) combined with a graphical approach, such as a Dynamic Chain Graph Model (DCGM). These dynamic graphical models were useful in assessing the role of estimating the brain network structure and describing its causal relationship. In addition, the class of DCGM was able to visualize and compare experimental conditions and brain frequency domains [using finite impulse response (FIR) filter]. Moreover, using multilayer networks, the results corroborate with the susceptibility of sparse dynamic models, bypassing the false positives problem in estimation algorithms. We conclude that applying sparse dynamic models to EEG data may be useful for describing intervention-relocated changes in brain connectivity.

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BrainWave 网络：稀疏动态模型是否容易受到大脑操纵实验的影响？

稀疏时间序列模型在估计同期和持续的大脑连接方面显示出前景。本文的动机是使用脑电图信号作为我们既定干预方案的结果的神经科学实验，这是一种神经康复的新方法，旨在开发中风后患者视觉垂直障碍的治疗方法。为了分析以更具体的方式反映神经网络功能和处理的 [复杂结果测量 (EEG)]，我们对稀疏时间序列模型（经典 VAR、GLASSO、TSCGM 和 TSCGM 修改与非-线性和迭代优化）结合图形方法，例如动态链图模型（DCGM）。这些动态图形模型有助于评估估计大脑网络结构和描述其因果关系的作用。此外，DCGM 类能够可视化和比较实验条件和大脑频域 [使用有限脉冲响应 (FIR) 滤波器]。此外，使用多层网络，结果证实了稀疏动态模型的敏感性，绕过了估计算法中的误报问题。我们得出结论，将稀疏动态模型应用于 EEG 数据可能有助于描述大脑连接的干预重新定位变化。结果证实了稀疏动态模型的敏感性，绕过了估计算法中的误报问题。我们得出结论，将稀疏动态模型应用于 EEG 数据可能有助于描述大脑连接的干预重新定位变化。结果证实了稀疏动态模型的敏感性，绕过了估计算法中的误报问题。我们得出结论，将稀疏动态模型应用于 EEG 数据可能有助于描述大脑连接的干预重新定位变化。