IEEE Trans Neural Syst Rehabil Eng. 2021;29:2605-2614. doi: 10.1109/TNSRE.2021.3133853. Epub 2021 Dec 21.
Brain-computer interface (BCI)-based stroke rehabilitation is an emerging field in which different studies have reported variable outcomes. Among the BCI paradigms, motor imagery (MI)-based closed-loop BCI is still the main pattern in rehabilitation training. It can estimate a patient' motor intention and provide corresponding feedback. However, the individual difference in the ability to generate event-related desynchronization (ERD) and the low classification accuracy of the multi-class scenario restrict the application of MI-based BCI. In the current study, a novel online action observation (AO)-based BCI was proposed. The visual stimuli of four types of hand movements were designed to simultaneously induce steady-state motion visual evoked potential (SSMVEP) in the occipital region and to activate the sensorimotor region. Task-related component analysis was performed to identify the SSMVEP. Results showed that the amplitude of the induced frequency in the SSMVEP had a negative relationship with the stimulus frequency. The classification accuracy in the four-class scenario reached 72.81 ± 13.55% within 2.5s. Importantly, the AO-based closed-loop BCI, which provided visual feedback based on the SSMVEP, could enhance ERD compared with AO-alone. The increased attentiveness might be one key factor for the enhancement of the ERD in the designed AO-based BCI. In summary, the proposed AO-based BCI provides a new insight for BCI-based rehabilitation.
基于脑机接口的中风康复是一个新兴领域，不同的研究报告了不同的结果。在脑机接口范式中，基于运动想象的闭环脑机接口仍然是康复训练的主要模式。它可以估计患者的运动意图并提供相应的反馈。然而，个体在产生事件相关去同步（ERD）的能力上存在差异，以及多类场景的分类准确性较低，限制了基于运动想象的脑机接口的应用。在当前的研究中，提出了一种新的基于在线动作观察（AO）的脑机接口。设计了四种手部运动的视觉刺激，以同时在枕区诱导稳态运动视觉诱发电位（SSMVEP），并激活感觉运动区。进行任务相关成分分析以识别 SSMVEP。结果表明，诱导的 SSMVEP 频率的幅度与刺激频率呈负相关。在 2.5s 内，四分类场景的分类准确率达到 72.81±13.55%。重要的是，基于 SSMVEP 的视觉反馈的 AO 闭环脑机接口可以增强 ERD，与仅 AO 相比。注意力的增加可能是增强设计的 AO 脑机接口中 ERD 的关键因素之一。总之，所提出的基于 AO 的脑机接口为基于脑机接口的康复提供了新的见解。
