Department of Neurosurgery, University Hospital Knappschaftskrankenhaus Bochum GmbH, Ruhr-University Bochum, Bochum, Germany.
Sci Rep. 2021 Feb 25;11(1):4614. doi: 10.1038/s41598-021-84288-5.
Invasive brain-computer-interfaces (BCIs) aim to improve severely paralyzed patient's (e.g. tetraplegics) quality of life by using decoded movement intentions to let them interact with robotic limbs. We argue that the performance in controlling an end-effector using a BCI depends on three major factors: decoding error, missing somatosensory feedback and alignment error caused by translation and/or rotation of the end-effector relative to the real or perceived body. Using a virtual reality (VR) model of an ideal BCI decoder with healthy participants, we found that a significant performance loss might be attributed solely to the alignment error. We used a shape-drawing task to investigate and quantify the effects of robot arm misalignment on motor performance independent from the other error sources. We found that a 90° rotation of the robot arm relative to the participant leads to the worst performance, while we did not find a significant difference between a 45° rotation and no rotation. Additionally, we compared a group of subjects with indirect haptic feedback with a group without indirect haptic feedback to investigate the feedback-error. In the group without feedback, we found a significant difference in performance only when no rotation was applied to the robot arm, supporting that a form of haptic feedback is another important factor to be considered in BCI control.
侵入性脑机接口 (BCI) 旨在通过解码运动意图，让严重瘫痪的患者（如四肢瘫痪者）使用机器人肢体进行交互，从而提高他们的生活质量。我们认为，使用 BCI 控制末端执行器的性能取决于三个主要因素：解码错误、缺失的感觉反馈以及末端执行器相对于真实或感知身体的平移和/或旋转引起的对准误差。我们使用具有健康参与者的理想 BCI 解码器的虚拟现实 (VR) 模型发现，仅对准误差可能导致显著的性能损失。我们使用形状绘制任务来独立于其他误差源研究和量化机器人手臂不对准对运动性能的影响。我们发现，与没有旋转相比，机器人手臂相对于参与者旋转 90°会导致最差的性能，而 45°旋转与没有旋转之间没有显著差异。此外，我们比较了一组具有间接触觉反馈的受试者和一组没有间接触觉反馈的受试者，以研究反馈误差。在没有反馈的组中，我们发现只有在机器人手臂没有旋转的情况下，性能才有显著差异，这表明触觉反馈的一种形式是 BCI 控制中需要考虑的另一个重要因素。
