Phang Chun-Ren, Chen Chia-Hsin, Cheng Yuan-Yang, Chen Yi-Jen, Ko Li-Wei
IEEE Trans Neural Syst Rehabil Eng. 2023;31:139-149. doi: 10.1109/TNSRE.2022.3217298. Epub 2023 Jan 30.
Motor-based brain-computer interfaces (BCIs) were developed from the brain signals during motor imagery (MI), motor preparation (MP), and motor execution (ME). Motor-based BCIs provide an active rehabilitation scheme for post-stroke patients. However, BCI based solely on MP was rarely investigated. Since MP is the precedence phase before MI or ME, MP-BCI could potentially detect brain commands at an earlier state. This study proposes a bipedal MP-BCI system, which is actuated by the reduction in frontoparietal connectivity strength. Three substudies, including bipedal classification, neurofeedback, and post-stroke analysis, were performed to validate the performance of our proposed model. In bipedal classification, functional connectivity was extracted by Pearson's correlation model from electroencephalogram (EEG) signals recorded while the subjects were performing MP and MI. The binary classification of MP achieved short-lived peak accuracy of 73.73(±7.99)% around 200-400 ms post-cue. The peak accuracy was found synchronized to the MP-related potential and the decrement in frontoparietal connection strength. The connection strengths of the right frontal and left parietal lobes in the alpha range were found negatively correlated to the classification accuracy. In the subjective neurofeedback study, the majority of subjects reported that motor preparation instead of the motor imagery activated the frontoparietal dysconnection. Post-stroke study also showed that patients exhibit lower frontoparietal connections compared to healthy subjects during both MP and ME phases. These findings suggest that MP reduced alpha band functional frontoparietal connectivity and the EEG signatures of left and right foot MP could be discriminated more effectively during this phase. A neurofeedback paradigm based on the frontoparietal network could also be utilized to evaluate post-stroke rehabilitation training.
基于运动的脑机接口(BCI)是根据运动想象(MI)、运动准备(MP)和运动执行(ME)期间的脑信号开发的。基于运动的BCI为中风后患者提供了一种积极的康复方案。然而,仅基于MP的BCI很少被研究。由于MP是MI或ME之前的优先阶段,MP-BCI有可能在更早的状态下检测到脑指令。本研究提出了一种双足MP-BCI系统,该系统由额顶叶连接强度的降低来驱动。进行了三项子研究,包括双足分类、神经反馈和中风后分析,以验证我们提出的模型的性能。在双足分类中,通过皮尔逊相关模型从受试者执行MP和MI时记录的脑电图(EEG)信号中提取功能连接。MP的二元分类在提示后约200-400毫秒达到了73.73(±7.99)%的短期峰值准确率。发现峰值准确率与MP相关电位以及额顶叶连接强度的降低同步。发现在α范围内右额叶和左顶叶的连接强度与分类准确率呈负相关。在主观神经反馈研究中,大多数受试者报告说运动准备而不是运动想象激活了额顶叶失连接。中风后研究还表明,与健康受试者相比,患者在MP和ME阶段的额顶叶连接都较低。这些发现表明,MP降低了α波段功能额顶叶连接,并且在此阶段可以更有效地辨别左右脚MP的EEG特征。基于额顶叶网络的神经反馈范式也可用于评估中风后的康复训练。