Abstract：
Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity\'s significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction\'s accuracy.
摘要：
目标：尽管基于运动想象的脑机接口（MI-BCI）具有巨大的潜力，它的实际应用面临着BCI文盲等挑战。为了缓解这个问题，研究人员试图通过使用静息状态来预测BCI-文盲，因为这被发现与BCI表现有关。随着连通性在神经科学中的重要性越来越大，BCI研究人员已经将连接性应用于它。然而,连通性问题尚未得到充分考虑。首先,尽管存在各种连通性度量，只有一些被用来预测BCI文盲。这是有问题的，因为每个度量都有不同的假设和观点来估计连通性，根据度量得出不同的结果。第二,频率范围影响连通性估计。此外,每个指标是否都有自己的最佳频率范围还不清楚。第三，估计连通性的方式可能会根据数据集的不同而有所不同。同时,我们仍然不知道静息状态脑电图网络在BCI识字率和文盲之间有何不同。
方法：为了解决上述问题，我们通过采用不同的图论度量,使用三个功能连通性(FC)和三个有效连通性(EC)度量,分析了三个大型公共EEG数据集.我们的分析表明，预测BCI文盲的适当频率范围因指标而异。发现Alpha范围适合于频域的度量，而α+θ被发现适用于多变量格兰杰因果关系(MVGC)。无论使用何种指标和数据集，BCI识字组和文盲组之间的网络效率差异都是恒定的。虽然我们观察到BCI识字有更强的连通性，没有发现其他显著的结构差异.
结论：根据我们的发现，我们预测了整个数据集的MI-BCI性能。我们发现结合几个图形特征可以提高预测的准确性。
方法：为了解决上述问题，我们通过采用不同的图论度量,使用三个功能连通性(FC)和三个有效连通性(EC)度量,分析了三个大型公共EEG数据集.我们的分析表明，预测BCI文盲的适当频率范围因指标而异。发现Alpha范围适合于频域的度量，而α+θ被发现适用于多变量格兰杰因果关系(MVGC)。无论使用何种指标和数据集，BCI识字组和文盲组之间的网络效率差异都是恒定的。虽然我们观察到BCI识字有更强的连通性，没有发现其他显著的结构差异.
结论：根据我们的发现，我们预测了整个数据集的MI-BCI性能。我们发现结合几个图形特征可以提高预测的准确性。
