IEEE Trans Neural Syst Rehabil Eng. 2020 Dec;28(12):2671-2680. doi: 10.1109/TNSRE.2020.3038657. Epub 2021 Jan 28.
Brain-computer interface (BCI) brings hope to patients suffering from neuromuscular diseases, by allowing the control of external devices using neural signals from the central nervous system. However, a portion of individuals was unable to operate BCI with high efficacy. This research aimed to study the brain-wide functional connectivity differences that contributed to BCI performance, and investigate the relationship between task-related connectivity strength and BCI performance. Functional connectivity was estimated using pairwise Pearson's correlation from the EEG of 48 subjects performing left or right hand motor imagery (MI) tasks. The classification accuracy of linear support vector machine (SVM) to distinguish both tasks were used to represent MI-BCI performance. The significant differences in connectivity strengths were examined using Welch's T-test. The association between accuracy and connection strength was studied using correlation model. Three intralobular and fourteen interlobular connections from the parietal lobe showed a correlation of 0.31 and -0.34 respectively. Results indicate that alpha wave connectivity from 8 Hz to 13 Hz was more related to classification performance compared to high-frequency waves. Subject-independent trial-based analysis shows that MI trials executed with stronger intralobular and interlobular parietal connections performed significantly better than trials with weaker connections. Further investigation from an independent MI dataset reveals several similar connections that were correlated with MI-BCI performance. The functional connectivity of the parietal lobe could potentially allow prediction of MI-BCI performance and enable implementation of neurofeedback training for users to improve the usability of MI-BCI.
脑机接口 (BCI) 通过利用中枢神经系统的神经信号来控制外部设备，为患有神经肌肉疾病的患者带来了希望。然而，一部分人无法高效地操作 BCI。本研究旨在研究导致 BCI 性能差异的大脑全脑功能连接差异，并探讨任务相关连接强度与 BCI 性能之间的关系。使用来自 48 名受试者执行左手或右手运动想象 (MI) 任务的 EEG 的成对 Pearson 相关系数来估计功能连接。使用线性支持向量机 (SVM) 的分类准确性来区分两种任务，以表示 MI-BCI 性能。使用 Welch's T 检验检查连接强度的显着差异。使用相关模型研究准确性和连接强度之间的关系。来自顶叶的三个小叶内和 14 个小叶间连接显示出分别为 0.31 和 -0.34 的相关性。结果表明，与高频波相比，8 Hz 到 13 Hz 的 alpha 波连接与分类性能更相关。基于独立试验的分析表明，执行时具有较强小叶内和小叶间顶叶连接的 MI 试验明显优于具有较弱连接的试验。来自独立 MI 数据集的进一步研究揭示了几个与 MI-BCI 性能相关的相似连接。顶叶的功能连接有可能能够预测 MI-BCI 性能，并实现神经反馈训练，以提高 MI-BCI 的可用性。
