Guerrero-Mendez Cristian David, Blanco-Diaz Cristian Felipe, Rivera-Flor Hamilton, Fabriz-Ulhoa Pedro Henrique, Fragoso-Dias Eduardo Antonio, de Andrade Rafhael Milanezi, Delisle-Rodriguez Denis, Bastos-Filho Teodiano Freire
Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitoria 29075-910, Brazil;
Graduate Program in Mechanical Engineering, Federal University of Espírito Santo (UFES), Vitoria 29075-910, Brazil;
NeuroSci. 2024 May 11;5(2):169-183. doi: 10.3390/neurosci5020012. eCollection 2024 Jun.
Common Spatial Pattern (CSP) has been recognized as a standard and powerful method for the identification of Electroencephalography (EEG)-based Motor Imagery (MI) tasks when implementing brain-computer interface (BCI) systems towards the motor rehabilitation of lost movements. The combination of BCI systems with robotic systems, such as upper limb exoskeletons, has proven to be a reliable tool for neuromotor rehabilitation. Therefore, in this study, the effects of temporal and frequency segmentation combined with layer increase for spatial filtering were evaluated, using three variations of the CSP method for the identification of passive movement vs. MI+passive movement. The passive movements were generated using a left upper-limb exoskeleton to assist flexion/extension tasks at two speeds (high-85 rpm and low-30 rpm). Ten healthy subjects were evaluated in two recording sessions using Linear Discriminant Analysis (LDA) as a classifier, and accuracy (ACC) and False Positive Rate (FPR) as metrics. The results allow concluding that the use of temporal, frequency or spatial selective information does not significantly ( 0.05) improve task identification performance. Furthermore, dynamic temporal segmentation strategies may perform better than static segmentation tasks. The findings of this study are a starting point for the exploration of complex MI tasks and their application to neurorehabilitation, as well as the study of brain effects during exoskeleton-assisted MI tasks.
当为恢复丧失运动功能进行脑机接口（BCI）系统的运动康复应用时，公共空间模式（CSP）已被公认为是一种用于识别基于脑电图（EEG）的运动想象（MI）任务的标准且强大的方法。BCI系统与诸如上肢外骨骼等机器人系统相结合，已被证明是神经运动康复的可靠工具。因此，在本研究中，我们评估了时间和频率分割与空间滤波的层增加相结合的效果，使用了三种CSP方法变体来识别被动运动与运动想象加被动运动。被动运动通过左上肢外骨骼以两种速度（高85转/分钟和低30转/分钟）辅助屈伸任务来产生。十名健康受试者在两个记录环节中接受评估，使用线性判别分析（LDA）作为分类器，并将准确率（ACC）和误报率（FPR）作为指标。结果表明，使用时间、频率或空间选择性信息并不能显著（ 0.05）提高任务识别性能。此外，动态时间分割策略可能比静态分割任务表现更好。本研究结果是探索复杂运动想象任务及其在神经康复中的应用以及研究外骨骼辅助运动想象任务期间大脑效应的起点。
