内模自适应卡尔曼滤波在视觉诱发脑电信号提取中的应用──提取视觉诱发脑电信号的新方法之五

Extracting Visual Evoked Potential via a Novel Internal Model Adaptive Kalman Filtering Approach

基于确定性有用信号和扰动各自的内模建立了视觉诱发脑电（ＶＥＰ）和自发脑电（ＥＥＧ）混合信号的扩展状态空间模型．然后，将利用非线性推广卡尔曼滤波迭代型算法所形成的内模自适应卡尔曼滤波算法用于提取ＶＥＰ信号．数字仿真和临床试用结果表明了该方法的有效性．

Based on the internal models of the deterministic signal or disturbance, the mixture of the visual evoked potential (VEP) and residual electro-encephalogram (EEG) is modeled by a set of augmented state-space equations. Then, an internal model adaptive kalman filtering (IMAKF) based on the non-linear extended kalman filtering iterative (EKF) algorithm is utilized for extracting the VEP with the EEG as an on-going biological background noise. Numerical simulation and clinical application have shown the effectiveness of the proposed approach.