Zhang Deyu, Liu Siyu, Zhang Jian, Li Guoqi, Suo Dingjie, Liu Tiantian, Luo Jiawei, Ming Zhiyuan, Wu Jinglong, Yan Tianyi
IEEE J Biomed Health Inform. 2022 Dec;26(12):6138-6149. doi: 10.1109/JBHI.2022.3219812. Epub 2022 Dec 8.
Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as "turn left" or "turn right", which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs.
A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated.
The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%).
This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.
脑机接口（BCI）已应用于二维（2D）导航机器人设备，如脑控轮椅和脑控车辆。然而，当代BCI系统由二元选择性控制驱动。一方面，从人类到机器只能传递方向信息，如“向左转”或“向右转”，这意味着诸如回转半径等量化值无法得到控制。在本研究中，我们提出了一种空间梯度BCI控制器及相应的环境协调器，通过该控制器，脑指令的量化值能够以二维向量的形式进行传递，提高了BCI的灵活性、稳定性和效率。
布置一组水平的稳态视觉刺激阵列以激发受试者的脑电（EEG）信号。将受试者脑电图（EEG）与刺激特征之间的协方差阵列映射为量化的二维向量。然后将生成的向量输入到预测控制器中，并与机器人预测环境协调器以向量计算形式生成的虚拟力相融合。接着将所得向量解释为机器人的驱动力，并生成实时速度反馈。
对于单障碍物避障任务，所提出的SGC控制器比选择性控制方法产生更快的响应（27.4秒对34.9秒）。在实际的多障碍物任务中，所提出的机器人在到达目标任务中的执行速度比选择性控制器快39%，并且在多障碍物避障任务中具有更好的鲁棒性（平均失败率从27%显著降至4%）。
本研究提出了一种新形式的脑机共享控制策略，该策略以二维控制向量流的形式而非选择性常量值对脑指令进行量化。结合预测环境协调器，优化了机器人的脑控策略并使其具有更高的灵活性。所提出的控制器可用于脑控二维导航设备，如脑控轮椅和车辆。
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