Objective. One of the main goals in brain–computer interface (BCI) research is the replacement or restoration of lost function in individuals with paralysis. One line of research investigates the inference of movement kinematics from brain activity during different volitional states. A growing number of electroencephalography (EEG) and magnetoencephalography (MEG) studies suggest that information about directional (e.g. velocity) and nondirectional (e.g. speed) movement kinematics is accessible noninvasively. We sought to assess if the neural information associated with both types of kinematics can be combined to improve the decoding accuracy. Approach. In an offline analysis, we reanalyzed the data of two previous experiments containing the recordings of 34 healthy participants (15 EEG, 19 MEG). We decoded 2D movement trajectories from low-frequency M/EEG signals in executed and observed tracking movements, and compared the accuracy of an unscented Kalman filter (UKF) that explicitly modeled the nonlinear relation between directional and nondirectional kinematics to the accuracies of linear Kalman (KF) and Wiener filters which did not combine both types of kinematics. Main results. At the group level, posterior-parietal and parieto-occipital (executed and observed movements) and sensorimotor areas (executed movements) encoded kinematic information. Correlations between the recorded position and velocity trajectories and the UKF decoded ones were on average 0.49 during executed and 0.36 during observed movements. Compared to the other filters, the UKF could achieve the best trade-off between maximizing the signal to noise ratio and minimizing the amplitude mismatch between the recorded and decoded trajectories. Significance. We present direct evidence that directional and nondirectional kinematic information is simultaneously detectable in low-frequency M/EEG signals. Moreover, combining directional and nondirectional kinematic information significantly improves the decoding accuracy upon a linear KF.

客观。脑机接口 (BCI) 研究的主要目标之一是替换或恢复瘫痪患者丧失的功能。一项研究调查了不同意志状态下大脑活动对运动运动学的推断。越来越多的脑电图 (EEG) 和脑磁图 (MEG) 研究表明，有关定向（例如速度）和非定向（例如速度）运动学的信息可以通过非侵入性方式获取。我们试图评估与两种运动学相关的神经信息是否可以结合起来以提高解码精度。方法. 在离线分析中，我们重新分析了之前两个实验的数据，其中包含 34 名健康参与者（15 名 EEG，19 名 MEG）的记录。我们从执行和观察到的跟踪运动中的低频 M/EEG 信号中解码了 2D 运动轨迹，并将明确建模定向和非定向运动学之间的非线性关系的无迹卡尔曼滤波器 (UKF) 的精度与线性卡尔曼的精度进行了比较。 KF) 和 Wiener 滤波器，它们没有结合两种类型的运动学。主要结果. 在群体层面，后顶叶和顶枕（执行和观察的运动）和感觉运动区域（执行的运动）编码运动学信息。记录的位置和速度轨迹与 UKF 解码的轨迹之间的相关性在执行期间平均为 0.49，在观察到的运动期间平均为 0.36。与其他滤波器相比，UKF 可以在最大化信噪比和最小化记录和解码轨迹之间的幅度失配之间取得最佳平衡。意义. 我们提供直接证据表明在低频 M/EEG 信号中可以同时检测到定向和非定向运动学信息。此外，结合方向和非方向运动学信息显着提高了线性 KF 的解码精度。