Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting
Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting
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DOI：
10.1109/jsen.2020.3005968
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发表时间：
2020-11-15
影响因子：
4.3
通讯作者：
Wilaiprasitporn, Theerawit
中科院分区：
文献类型：
--
作者：
Chaisaen, Rattanaphon;Autthasan, Phairot;Wilaiprasitporn, Theerawit
事件相关去同步化（ERD/S）和运动相关皮层电位（MRCP）在下肢康复的脑-机接口（BCI）中起着重要的作用，尤其是在站立和坐位时。然而，人们对站立和坐姿之间皮层激活的差异知之甚少，特别是大脑的意图如何调节运动前的感觉运动节奏，就像他们对切换运动所做的那样。在这项研究中，我们的目的是调查连续EEG节律的解码过程中的动作观察（AO），运动想象（MI），和运动执行（ME）的行动，站着和坐着。我们开发了一个行为任务，其中参与者被指示执行AO和MI/ME关于从坐到站和从站到坐的过渡动作。我们的研究结果表明，ERD是突出的AO期间，而ERS是典型的MI期间在整个感觉运动区的α波段。将滤波器组公共空间模式（FBCSP）和支持向量机（SVM）相结合用于分类，用于离线和分类器测试分析。离线分析表明，AO和MI的分类在从站立到坐下的转换中提供了最高的平均准确度，为82.73 +/- 2.54%。通过应用分类器测试分析，我们证明了更高的性能解码的神经意图从MI范式相比，ME范式。这些观察使我们看到了使用我们开发的基于AO和MI集成的任务来构建未来基于外骨骼的康复系统的有希望的方面。
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 +/- 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.