Despite the growing interest in brain–machine interface (BMI)-driven neuroprostheses, the translation of the BMI output into a suitable control signal for the robotic device is often neglected. In this article, we propose a novel control approach based on dynamical systems that was explicitly designed to take into account the nature of the BMI output that actively supports the user in delivering real-valued commands to the device and, at the same time, reduces the false positive rate. We hypothesize that such a control framework would allow users to continuously drive a mobile robot and it would enhance the navigation performance. 13 healthy users evaluated the system during three experimental sessions. Users exploit a 2-class motor imagery BMI to drive the robot to five targets in two experimental conditions: with a discrete control strategy, traditionally exploited in the BMI field, and with the novel continuous control framework developed herein. Experimental results show that the new approach: 1) allows users to continuously drive the mobile robot via BMI; 2) leads to significant improvements in the navigation performance; and 3) promotes a better coupling between user and robot. These results highlight the importance of designing a suitable control framework to improve the performance and the reliability of BMI-driven neurorobotic devices.

中文翻译：

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脑机接口驱动的移动机器人连续遥操作控制框架的作用

尽管人们对脑机接口 (BMI) 驱动的神经假体越来越感兴趣，但将 BMI 输出转换为适合机器人设备的控制信号的过程常常被忽视。在本文中，我们提出了一种基于动态系统的新型控制方法，该方法明确设计为考虑 BMI 输出的性质，该方法积极支持用户向设备发送实值命令，同时减少误报率。我们假设这样的控制框架将允许用户连续驱动移动机器人并提高导航性能。13 名健康用户在三个实验阶段对系统进行了评估。用户利用 2 类运动图像 BMI 在两个实验条件下将机器人驱动到五个目标：使用离散控制策略，传统上在 BMI 领域开发，并在此处开发了新颖的连续控制框架。实验结果表明，新方法：1）允许用户通过BMI连续驱动移动机器人；2) 导致导航性能的显着改进；3）促进用户和机器人之间更好的耦合。这些结果强调了设计合适的控制框架以提高 BMI 驱动的神经机器人设备的性能和可靠性的重要性。