Background

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.

New Method

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).

Results

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.

中文翻译：

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使用局部场电势信号的人机界面正则化卡尔曼滤波器

背景

脑机接口（BCI）寻求建立从脑到计算机的直接连接，以用于运动假体控制，监视器上的光标控制等应用。因此，从BCI中脑信号进行运动解码的准确性至关重要。卡尔曼滤波器（KF）通常用于BCI系统中，以解码神经活动并估计动力学和运动学参数。要使用KF，必须事先知道状态转换矩阵，观察矩阵以及过程噪声和测量噪声的协方差矩阵，但是，在许多应用中，这些矩阵是未知的。通常，为了估计这些参数，使用了普通最小二乘法和样本协方差矩阵估计器。

新方法

在这里，我们提出了正则化卡尔曼滤波器（RKF），它实现了两个基本特征：1）正则化状态方程的回归估计以改进状态转换矩阵的估计，以及2）使用收缩法改进对状态方程的估计。未知的测量噪声协方差矩阵。我们使用从猴子的运动皮层（手运动期间的运动学参数估计）和三只大鼠（手所施加的力的估计作为动力学参数）获得的两个局部场电数据集验证了该方法的性能。

结果

结果表明，所提出的方法优于传统的KF，具有特征选择的KF，偏最小二乘和Ridge回归方法。