Lee Minji, Yoon Jae-Geun, Lee Seong-Whan
Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.
Department of Artificial Intelligence, Korea University, Seoul, South Korea.
Front Hum Neurosci. 2020 Aug 6;14:321. doi: 10.3389/fnhum.2020.00321. eCollection 2020.
Motor imagery-based brain-computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines-but does not perform-a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as "BCI-inefficiency," in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1: = 0.54; Session 2: = 0.42). We also predicted MI performance using linear regression based on this connection ( = 0.31). The proposed predictors, based on dynamic causal modeling, can develop new strategies for improving BCI performance. These findings can further our understanding of BCI-inefficiency and help BCI users to lower costs and save time.
基于运动想象的脑机接口（MI-BCI）利用受试者想象（但不执行）特定动作时记录的大脑活动向计算机发送指令。然而，在不同受试者和实验中，大脑信号的变化会导致MI-BCI性能不一致；这被认为是实际脑机接口中的一个重大问题。此外，一些受试者表现出一种被称为“BCI低效”的现象，即他们无法产生用于BCI控制的大脑信号。这些受试者在使用BCI时存在很大困难。本研究的主要目标是确定影响MI性能的静息态网络连接，并利用这些连接预测MI性能。我们使用了一个公开的MI数据库，其中包括心理问卷结果和在不同日期分两阶段进行的实验前静息态数据。使用动态因果模型计算具有方向性的脑区之间的耦合强度。具体而言，我们研究了静息态下的运动网络，包括执行运动规划的背外侧前额叶皮层。结果，我们观察到低MI性能组和高MI性能组之间从辅助运动区到右侧背外侧前额叶皮层的连接强度存在显著差异。在静息态下测量的这种耦合，在高MI性能组中比低MI性能组显著更强。连接强度与MI-BCI性能呈正相关（第一阶段： = 0.54；第二阶段： = 0.42）。我们还基于这种连接使用线性回归预测MI性能（ = 0.31）。基于动态因果建模提出的预测因子可以开发提高BCI性能的新策略。这些发现可以加深我们对BCI低效的理解，并帮助BCI用户降低成本和节省时间。
