Neuroscience and Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16846-13114, Tehran, Iran.
Neuroscience and Neuroengineering Research Lab, Biomedical Engineering Department, School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16846-13114, Tehran, Iran.
J Neurosci Methods. 2021 Feb 15;350:109022. doi: 10.1016/j.jneumeth.2020.109022. Epub 2020 Dec 5.
Brain-computer interfaces (BCIs) seek to establish a direct connection from brain to computer, to use in applications such as motor prosthesis control, control of a cursor on the monitor, and so on. Hence, the accuracy of movement decoding from brain signals in BCIs is crucial. The Kalman filter (KF) is often used in BCI systems to decode neural activity and estimate kinetic and kinematic parameters. To use the KF, the state transition matrix, the observation matrix and the covariance matrices of the process and measurement noises must be known in advance, however, in many applications these matrices are not known. Typically, to estimate these parameters, the ordinary least squares method and the sample covariance matrix estimator are used. Our purpose is to enhance the decoding performance of the KF in BCI systems by improving the estimation of the mentioned parameters.
Here, we propose the Regularized Kalman Filter (RKF) which implements two fundamental features: 1) Regularizing the regression estimate of the state equation to improve the estimation of the state transition matrix, and 2) Use of shrinkage method to improve the estimation of the unknown measurement noise covariance matrix. We validated the performance of the proposed method using two datasets of local field potentials obtained from motor cortex of a monkey (Estimation of kinematic parameters during hand movement) and three rats (Estimation of the amount of force applied by hand as a kinetic parameter).
The results demonstrate that the proposed method outperforms the conventional KF, the KF with feature selection, the Partial least squares, and the Ridge regression approaches.
脑机接口（BCIs）旨在建立大脑与计算机之间的直接连接，用于运动假肢控制、监视器上光标控制等应用。因此，BCI 中从脑信号解码运动的准确性至关重要。卡尔曼滤波器（KF）常用于 BCI 系统中解码神经活动并估计运动学和运动学参数。为了使用 KF，必须事先知道状态转移矩阵、观测矩阵以及过程和测量噪声的协方差矩阵，但在许多应用中这些矩阵是未知的。通常，使用普通最小二乘法和样本协方差矩阵估计器来估计这些参数。我们的目的是通过改进上述参数的估计来提高 KF 在 BCI 系统中的解码性能。
在这里，我们提出了正则化卡尔曼滤波器（RKF），它实现了两个基本功能：1）正则化状态方程的回归估计，以改善状态转移矩阵的估计，2）使用收缩方法改善未知测量噪声协方差矩阵的估计。我们使用来自猴子运动皮层的局部场电位的两个数据集（手运动过程中的运动学参数估计）和三只大鼠（作为动力学参数的手施加力的量的估计）验证了所提出方法的性能。
结果表明，所提出的方法优于传统 KF、具有特征选择的 KF、偏最小二乘法和岭回归方法。
