基于改进p向量算法的诱发电位单历元自适应估计

R. Williams, J. Westerkamp
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引用次数: 0

摘要

提出了一种新的自适应滤波算法(改进的p向量算法)和特殊的多级滤波结构来解决单历元诱发响应的逐历元变化。首先将诱发反应建模为三个信号分量的和;所有时代的恒定集合平均值(M),噪声(N)和逐时代随机信号变化(Q)。两阶段时间序列自适应滤波器结构将每个新信号矢量的M和Q分量解耦。结果是提高了收敛性能。改进的p向量算法(mPa)消除了对单独期望信号的需要。因此,滤波器的输入也可以用作期望的或训练的信号。利用模拟数据集和人类数据集对mPa自适应滤波器进行了测试。mPa滤波器能够逐历元解析信号变化
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Single epoch adaptive estimation of evoked potentials using the modified p-vector algorithm
A new adaptive filtering algorithm (the modified P-vector algorithm) and special multistage filter structure was developed to resolve epoch-by-epoch variations in single epoch evoked responses. The evoked responses were first modeled as the sum of three signal components; a constant ensemble average (M) across all epochs, noise (N), and an epoch-by-epoch stochastic signal variation (Q). A two stage time sequenced adaptive filter structure decouples the M and Q components of each new signal vector. The result is improved convergence performance. The modified P-vector algorithm (mPa) was developed to eliminate the need for a separate desired signal. As a result, the filter input can also be used as the desired or training signal. The mPa adaptive filter was tested using simulated and human data sets. The mPa filter was able to resolve signal variations on an epoch-by-epoch basis.<>
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