Yao Yuchen, Swindlehurst A Lee
Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4365-8. doi: 10.1109/IEMBS.2011.6091083.
This paper presents two new algorithms based on the Extended Kalman Filter (EKF) for tracking the parameters of single dynamic magnetoencephalography (MEG) dipole sources. We assume a dynamic MEG dipole source with possibly both time-varying location and dipole orientation. The standard EKF-based tracking algorithm performs well under the assumption that the dipole source components vary in time as a Gauss-Markov process, provided that the background noise is temporally stationary. We propose a Projected-EKF algorithm that is adapted to a more forgiving condition where the background noise is temporally nonstationary, as well as a Projected-GLS-EKF algorithm that works even more universally, when the dipole components vary arbitrarily from one sample to the next.
本文提出了两种基于扩展卡尔曼滤波器(EKF)的新算法,用于跟踪单动态脑磁图(MEG)偶极子源的参数。我们假设一个动态MEG偶极子源,其位置和偶极子方向可能随时间变化。基于标准EKF的跟踪算法在偶极子源分量随时间作为高斯 - 马尔可夫过程变化的假设下表现良好,前提是背景噪声在时间上是平稳的。我们提出了一种投影EKF算法,它适用于背景噪声在时间上非平稳的更宽松条件,以及一种投影广义最小二乘EKF算法,当偶极子分量在不同样本之间任意变化时,该算法甚至能更普遍地工作。