论文标题
在静止状态下，基于默认模式网络的运动图像性能的有效关联
Effective Correlates of Motor Imagery Performance based on Default Mode Network in Resting-State
论文作者
论文摘要
基于运动图像的大脑计算机界面（MI-BCI）可以通过想象不同的肌肉运动来控制设备和通信。但是，大多数研究都报告了“ BCI省略”的问题，该问题没有足够的性能来使用Mi-BCI。因此，了解性能差并找到绩效变化的原因仍然是一个重要的挑战。在这项研究中，我们提出了MI性能的预测指标，并在静息状态脑电图中使用有效的连通性。结果，高和低的MI性能组与23％MI性能差异有显着差异。我们还发现，从右侧顶叶到静止状态脑室的左侧顶叶的连接与MI性能显着相关（r = -0.37）。这些发现可能有助于理解BCI的千古，并考虑适合该主题的替代方案。
Motor imagery based brain-computer interfaces (MI-BCIs) allow the control of devices and communication by imagining different muscle movements. However, most studies have reported a problem of "BCI-illiteracy" that does not have enough performance to use MI-BCI. Therefore, understanding subjects with poor performance and finding the cause of performance variation is still an important challenge. In this study, we proposed predictors of MI performance using effective connectivity in resting-state EEG. As a result, the high and low MI performance groups had a significant difference as 23% MI performance difference. We also found that connection from right lateral parietal to left lateral parietal in resting-state EEG was correlated significantly with MI performance (r = -0.37). These findings could help to understand BCI-illiteracy and to consider alternatives that are appropriate for the subject.
