Objective. The goal of this work is to identify the spatio-temporal facets of state-of-the-art electroencephalography (EEG)-based continuous neurorobotics that need to be addressed, prior to deployment in practical applications at home and in the clinic. Approach. Nine healthy human subjects participated in five sessions of one-dimensional (1D) horizontal (LR), 1D vertical (UD) and two-dimensional (2D) neural tracking from EEG. Users controlled a robotic arm and virtual cursor to continuously track a Gaussian random motion target using EEG sensorimotor rhythm modulation via motor imagery (MI) commands. Continuous control quality was analyzed in the temporal and spatial domains separately. Main results. Axis-specific errors during 2D tasks were significantly larger than during 1D counterparts. Fatigue rates were larger for control tasks with higher cognitive demand (LR, left- and right-hand MI) compared to those with lower cognitive demand (UD, both hands MI and rest). Additionally robotic arm and virtual cursor control exhibited equal tracking error during all tasks. However, further spatial error analysis of 2D control revealed a significant reduction in tracking quality that was dependent on the visual interference of the physical device. In fact, robotic arm performance was significantly greater than that of virtual cursor control when the users’ sightlines were not obstructed. Significance. This work emphasizes the need for practical interfaces to be designed around real-world tasks of increased complexity. Here, the dependence of control quality on cognitive task demand emphasizes the need for decoders that facilitate the translation of 1D task mastery to 2D control. When device footprint was accounted for, the introduction of a physical robotic arm improved control quality, likely due to increased user engagement. In general, this work demonstrates the need to consider both the physical footprint of devices, the complexity of training tasks, and the synergy of control strategies during the development of neurorobotic control.

客观的。这项工作的目标是确定在家庭和诊所的实际应用中部署之前需要解决的最先进的基于脑电图 (EEG) 的连续神经机器人的时空方面。方法。九名健康人类受试者参加了五次脑电图一维 (1D) 水平 (LR)、一维垂直 (UD) 和二维 (2D) 神经跟踪。用户通过运动想象（MI）命令控制机械臂和虚拟光标，使用脑电图感觉运动节律调制来连续跟踪高斯随机运动目标。分别在时间和空间域上分析连续控制质量。主要结果。 2D 任务期间的轴特定误差明显大于 1D 任务期间的轴特定误差。与认知需求较低（UD、双手 MI 和休息）的控制任务相比，认知需求较高（LR、左手和右手 MI）的控制任务的疲劳率更高。此外，机械臂和虚拟光标控制在所有任务中都表现出相同的跟踪误差。然而，对 2D 控制的进一步空间误差分析表明，跟踪质量显着降低，这取决于物理设备的视觉干扰。事实上，当用户视线不受遮挡时，机械臂的性能明显优于虚拟光标控制。意义。这项工作强调需要围绕日益复杂的现实世界任务来设计实用的界面。在这里，控制质量对认知任务需求的依赖性强调了对有助于将 1D 任务掌握转化为 2D 控制的解码器的需求。 当考虑到设备占用空间时，物理机械臂的引入提高了控制质量，这可能是由于用户参与度的增加。总的来说，这项工作表明在神经机器人控制的开发过程中需要考虑设备的物理足迹、训练任务的复杂性以及控制策略的协同作用。