Brain-Computer interfacing ( BCI ) has currently added a new dimension in assistive robotics. Existing brain-computer interfaces designed for position control applications suffer from two fundamental limitations. First, most of the existing schemes employ open-loop control, and thus are unable to track positional errors, resulting in failures in taking necessary online corrective actions. There are examples of a few works dealing with closed-loop electroencephalography ( EEG ) -based position control. These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule, which often creates a bottleneck preventing time-efficient control. Second, the existing brain-induced position controllers are designed to generate a position response like a traditional first-order system, resulting in a large steady-state error. This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential ( SSVEP ) induced link-selection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots / undershoots to reduce steady-state errors. Other than the above, the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors. Experiments undertaken reveal that the steady-state error is reduced to 0.2 % . The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.

中文翻译：

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用于机器人手臂闭环位置控制的混合脑机接口

脑机接口（BCI）当前在辅助机器人技术中增加了新的领域。设计用于位置控制应用程序的现有脑机接口有两个基本限制。首先，大多数现有方案采用开环控制，因此无法跟踪位置误差，从而导致无法采取必要的在线纠正措施。有一些有关基于闭环脑电图（EEG）的位置控制的著作的例子。这些现有的闭环大脑诱发的位置控制方案采用固定顺序的链接选择规则，该规则通常会产生阻止高效控制的瓶颈。其次，现有的大脑诱导位置控制器被设计为产生类似于传统一阶系统的位置响应，导致较大的稳态误差。本文通过有效控制所需的任意顺序保持稳态视觉诱发电位（SSVEP）诱导的链路选择的规定并生成位置控制系统的二阶响应并逐渐减小过冲来克服以上两个限制/下冲以减少稳态误差。除上述之外，第三项创新是利用运动图像和P300信号设计用于所述应用的混合式脑机接口系统，该系统使用位置误差过零时的速度反转来逐渐减小误差范围。进行的实验表明，稳态误差降低到0.2％。