论文标题
在动作观察,运动图像和执行时,对脑电图解码
Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting
论文作者
论文摘要
与事件相关的非同步和同步(ERD/S)和与运动相关的皮质电位(MRCP)在脑部计算机界面(BCI)中起着重要作用,用于下肢康复,尤其是在站立和坐姿中。但是,对站立和坐姿之间的皮质激活的差异知之甚少,尤其是大脑的意图如何调节运动前感觉运动节奏,就像它们在切换运动中所做的那样。在这项研究中,我们旨在调查在动作观察(AO),运动图像(MI)和电动执行(ME)中的连续脑电图的解码(ME),以实现站立和坐姿的作用。我们制定了一项行为任务,在该任务中,指示参与者在坐姿和立场的过渡动作方面同时执行AO和MI/ME。我们的结果表明,在AO期间,ERD是突出的,而ERS在MI期间的Alpha频带横跨感觉运动区域是典型的。滤波器库公共空间模式(FBCSP)和支持向量机(SVM)的组合用于离线和分类器测试分析。离线分析表明,AO和MI的分类为82.73 $ \ pm $ 2.54 \%的最高平均准确性在固定式过渡中。通过应用分类器测试分析,我们证明了与MI范式相比,MI范式的神经意图的更高性能。这些观察结果使我们陷入了基于AO和MI的整合来建立未来基于外骨骼的康复系统的有前途的方面。
Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially how the brain's intention modulates the pre-movement sensorimotor rhythm as they do for switching movements. In this study, we aim to investigate the decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for the actions of standing and sitting. We developed a behavioral task in which participants were instructed to perform both AO and MI/ME in regard to the transitioning actions of sit-to-stand and stand-to-sit. Our results demonstrated that the ERD was prominent during AO, whereas ERS was typical during MI at the alpha band across the sensorimotor area. A combination of the filter bank common spatial pattern (FBCSP) and support vector machine (SVM) for classification was used for both offline and classifier testing analyses. The offline analysis indicated the classification of AO and MI providing the highest mean accuracy at 82.73$\pm$2.54\% in the stand-to-sit transition. By applying the classifier testing analysis, we demonstrated the higher performance of decoding neural intentions from the MI paradigm in comparison to the ME paradigm. These observations led us to the promising aspect of using our developed tasks based on the integration of both AO and MI to build future exoskeleton-based rehabilitation systems.