Institute of Neural Engineering, Graz University of Technology, Graz 8010, Styria, Austria. Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna 40136, Italy.
J Neural Eng. 2020 Aug 11;17(4):046031. doi: 10.1088/1741-2552/aba6f7.
Continuous decoding of voluntary movement is desirable for closed-loop, natural control of neuroprostheses. Recent studies showed the possibility to reconstruct the hand trajectories from low-frequency (LF) electroencephalographic (EEG) signals. So far this has only been performed offline. Here, we attempt for the first time continuous online control of a robotic arm with LF-EEG-based decoded movements.
The study involved ten healthy participants, asked to track a moving target by controlling a robotic arm. At the beginning of the experiment, the robot was fully controlled by the participant's hand trajectories. After calibrating the decoding model, that control was gradually replaced by LF-EEG-based decoded trajectories, first with 33%, 66% and finally 100% EEG control. Likewise with other offline studies, we regressed the movement parameters (two-dimensional positions, velocities, and accelerations) from the EEG with partial least squares (PLS) regression. To integrate the information from the different movement parameters, we introduced a combined PLS and Kalman filtering approach (named PLSKF).
We obtained moderate yet overall significant (α = 0.05) online correlations between hand kinematics and PLSKF-decoded trajectories of 0.32 on average. With respect to PLS regression alone, the PLSKF had a stable correlation increase of Δr = 0.049 on average, demonstrating the successful integration of different models. Parieto-occipital activations were highlighted for the velocity and acceleration decoder patterns. The level of robot control was above chance in all conditions. Participants finally reported to feel enough control to be able to improve with training, even in the 100% EEG condition.
Continuous LF-EEG-based movement decoding for the online control of a robotic arm was achieved for the first time. The potential bottlenecks arising when switching from offline to online decoding, and possible solutions, were described. The effect of the PLSKF and its extensibility to different experimental designs were discussed.
连续解码自主运动对于神经假肢的闭环、自然控制是理想的。最近的研究表明，从低频（LF）脑电（EEG）信号重建手轨迹是可能的。到目前为止，这仅在离线状态下进行。在这里，我们首次尝试使用基于 LF-EEG 的解码运动进行连续在线控制机器人臂。
这项研究涉及 10 名健康参与者，要求他们通过控制机器人臂跟踪移动目标。在实验开始时，机器人完全由参与者的手轨迹控制。在对解码模型进行校准后，首先以 33%、66%和最终 100%的 EEG 控制逐渐取代基于 LF-EEG 的解码轨迹。与其他离线研究一样，我们使用偏最小二乘（PLS）回归从 EEG 回归运动参数（二维位置、速度和加速度）。为了整合不同运动参数的信息，我们引入了一种组合 PLS 和卡尔曼滤波方法（称为 PLSKF）。
我们获得了中等但总体显著（α=0.05）的在线相关性，平均手运动学与 PLSKF 解码轨迹之间的相关性为 0.32。与单独的 PLS 回归相比，PLSKF 的平均相关系数稳定增加了Δr=0.049，证明了不同模型的成功整合。对于速度和加速度解码器模式，突显了顶枕部激活。在所有条件下，机器人控制水平均高于随机水平。参与者最终报告说，他们感觉有足够的控制能力，可以通过训练进行提高，即使在 100%EEG 条件下也是如此。
首次实现了基于 LF-EEG 的连续运动解码，用于在线控制机器人臂。描述了从离线解码切换到在线解码时出现的潜在瓶颈及其可能的解决方案。讨论了 PLSKF 的效果及其对不同实验设计的可扩展性。
