Action observation (AO)-based brain-computer interface (BCI) is an important technology in stroke rehabilitation training. It has the advantage of simultaneously inducing steady-state motion visual evoked potential (SSMVEP) and activating sensorimotor rhythm. Moreover, SSMVEP could be utilized to perform classification. However, SSMVEP is composed of complex modulation frequencies. Traditional canonical correlation analysis (CCA) suffers from poor recognition performance in identifying those modulation frequencies at short stimulus duration. To address this issue, task-related component analysis (TRCA) was utilized to deal with SSMVEP for the first time. An interesting phenomenon was found: different modulated frequencies in SSMVEP distributed in different task-related components. On this basis, a multi-component TRCA method was proposed. All the significant task-related components were utilized to construct multiple spatial filters to enhance the detection of SSMVEP. Further, a combination of TRCA and CCA was proposed to utilize both advantages. Results showed that the accuracies using the proposed methods were significant higher than that using CCA at all window lengths and significantly higher than that using ensemble-TRCA at short window lengths (≤2 s). Therefore, the proposed methods further validate the induced modulation frequencies and will speed up the application of the AO-based BCI in rehabilitation.

中文翻译：

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基于任务相关成分分析的动作观察刺激诱导的SSMVEP检测增强

基于动作观察（AO）的脑机接口（BCI）是脑卒中康复训练中的一项重要技术。它具有同时诱导稳态运动视觉诱发电位 (SSMVEP) 和激活感觉运动节律的优点。此外，SSMVEP 可用于执行分类。然而，SSMVEP 是由复杂的调制频率组成的。传统的典型相关分析 (CCA) 在识别短刺激持续时间的调制频率时识别性能较差。为了解决这个问题，首次利用任务相关组件分析（TRCA）来处理 SSMVEP。发现了一个有趣的现象：SSMVEP 中不同的调制频率分布在不同的任务相关组件中。在此基础上，提出了一种多分量TRCA方法。利用所有与任务相关的重要组件来构建多个空间滤波器，以增强对 SSMVEP 的检测。此外，提出了 TRCA 和 CCA 的组合以利用这两个优点。结果表明，使用所提出的方法的准确度在所有窗口长度下均显着高于使用 CCA，并在短窗口长度（≤2 s）下显着高于使用 ensemble-TRCA。因此，所提出的方法进一步验证了诱导调制频率，并将加速基于 AO 的 BCI 在康复中的应用。结果表明，使用所提出的方法的准确度在所有窗口长度下均显着高于使用 CCA，并在短窗口长度（≤2 s）下显着高于使用 ensemble-TRCA。因此，所提出的方法进一步验证了诱导调制频率，并将加速基于 AO 的 BCI 在康复中的应用。结果表明，使用所提出的方法的准确度在所有窗口长度下均显着高于使用 CCA，并在短窗口长度（≤2 s）下显着高于使用 ensemble-TRCA。因此，所提出的方法进一步验证了诱导调制频率，并将加速基于 AO 的 BCI 在康复中的应用。