Korea Advanced Institute of Science and Technology, Bio and Brain Engineering, 411 E16-1(YBS Building) Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.
Seoul National University College of Natural Sciences, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea.
J Neural Eng. 2022 Sep 7;19(5). doi: 10.1088/1741-2552/ac8b37.
. Reaching hand movement is an important motor skill actively examined in the brain-computer interface (BCI). Among the various components of movement analyzed is the hand's trajectory, which describes the hand's continuous positions in three-dimensional space. While a large body of studies have investigated the decoding of real movements and the reconstruction of real hand movement trajectories from neural signals, fewer studies have attempted to decode the trajectory of the imagined hand movement. To develop BCI systems for patients with hand motor dysfunctions, the systems essentially have to achieve movement-free control of external devices, which is only possible through successful decoding of purely imagined hand movement.. To achieve this goal, this study used a machine learning technique (i.e. the variational Bayesian least square) to analyze the electrocorticogram (ECoG) of 18 epilepsy patients obtained from when they performed movement execution (ME) and kinesthetic movement imagination (KMI) of the reach-and-grasp hand action.. The variational Bayesian decoding model was able to successfully predict the imagined trajectories of the hand movement significantly above the chance level. The Pearson's correlation coefficient between the imagined and predicted trajectories was 0.3393 and 0.4936 for the KMI (KMI trials only) and MEKMI paradigm (alternating trials of ME and KMI), respectively.. This study demonstrated a high accuracy of prediction for the trajectories of imagined hand movement, and more importantly, a higher decoding accuracy of the imagined trajectories in the MEKMI paradigm compared to the KMI paradigm solely.
伸手运动是脑机接口（BCI）中积极研究的重要运动技能。在分析的运动的各种组成部分中，手的轨迹描述了手在三维空间中的连续位置。虽然大量研究已经研究了真实运动的解码和从神经信号重建真实手运动轨迹，但很少有研究试图解码想象中的手运动轨迹。为了为手部运动功能障碍的患者开发 BCI 系统，这些系统本质上必须实现对外置设备的无运动控制，而这只有通过成功解码纯粹想象中的手部运动才能实现。为了实现这一目标，本研究使用了机器学习技术（即变分贝叶斯最小二乘法）来分析 18 名癫痫患者的脑皮层电图（ECoG），这些患者在进行手部执行运动（ME）和手部抓握动作的运动想象（KMI）时获得了这些数据。变分贝叶斯解码模型能够成功地预测手部运动的想象轨迹，显著高于随机水平。想象轨迹与预测轨迹之间的 Pearson 相关系数分别为 0.3393 和 0.4936，用于 KMI（仅 KMI 试验）和 MEKMI 范式（ME 和 KMI 交替试验）。本研究证明了对想象中的手部运动轨迹进行高度准确的预测，更重要的是，与仅 KMI 范式相比，在 MEKMI 范式中对想象轨迹的解码精度更高。
