Institute of Neural Engineering, Graz University of Technology, Graz 8010, Styria, Austria.
Department of Neurological Diagnosis and Restoration, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
J Neural Eng. 2020 Nov 4;17(5):056027. doi: 10.1088/1741-2552/abb3b3.
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.
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.
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.
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.
脑-机接口（BCI）研究的主要目标之一是为瘫痪患者恢复或替代丧失的功能。其中一个研究方向是从不同随意状态的大脑活动中推断运动运动学。越来越多的脑电图（EEG）和脑磁图（MEG）研究表明，非侵入性地获取关于方向（例如速度）和非方向（例如速度）运动运动学的信息是可行的。我们试图评估与这两种运动学相关的神经信息是否可以组合以提高解码精度。
在离线分析中，我们重新分析了包含 34 名健康参与者（15 名 EEG，19 名 MEG）记录的两项先前实验的数据。我们从执行和观察跟踪运动中的低频 M/EEG 信号中解码 2D 运动轨迹，并比较了明确建模方向和非方向运动学之间非线性关系的无迹卡尔曼滤波器（UKF）的准确性与不组合两种运动学的线性卡尔曼（KF）和维纳滤波器的准确性。
在组水平上，后顶叶和顶枕叶（执行和观察运动）以及感觉运动区（执行运动）编码了运动学信息。记录位置和速度轨迹与 UKF 解码轨迹之间的相关性在执行运动时平均为 0.49，在观察运动时平均为 0.36。与其他滤波器相比，UKF 可以在最大程度地提高信噪比和最小化记录轨迹和解码轨迹之间的幅度失配之间取得最佳折衷。
我们提供了直接证据，表明在低频 M/EEG 信号中可以同时检测到方向和非方向运动学信息。此外，结合方向和非方向运动学信息可以显著提高线性 KF 的解码精度。
