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首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >IMPROVED NONLINEAR NOISE ESTIMATION IN EEG SIGNAL BASED ON VOLTERRA FILTERS
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IMPROVED NONLINEAR NOISE ESTIMATION IN EEG SIGNAL BASED ON VOLTERRA FILTERS

机译：基于Volterra滤波器的EEG信号中的改善非线性噪声估计

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摘要

Recognition and compensation of undesired nonlinearity is one of the important subjects in the field of digital signal processing. The Volterra model is widely used for nonlinearity identification in practical applications. The current tendency in the digital systems design is the identification and compensation of unwanted nonlinearities. In this paper, we employed a nonlinear noise estimation approach for electroencephalogram (EEG) signal based on a combination of linear predictive coding (LPC) and Volterra filter that is a new and good way to estimate noise in EEG signal. We initially used LPC filter to estimate the noise present in EEG signal (correlated and uncorrelated noise) plus the uncorrelated portion of the signal (the part of the signal that has no linear relation to its past samples). After that, we employed nonlinear Volterra model to estimate the existing noise in EEG signal (correlated and uncorrelated noise). We show that by employing the cascade of LPC and Volterra filter, we can considerably improve the signal-to-noise ratio (SNR) in EEG signal by the ratio of at least 1.94. Also, we compared the simulation results to the case where we used just Volterra filter. In comparison with just Volterra filter, we have a significant increase in the SNR.
机译：对不期望的非线性的识别和补偿是数字信号处理领域的重要科目之一。 Volterra模型广泛用于实际应用中的非线性鉴定。数字系统设计的目前趋势是不需要的非线性的识别和补偿。在本文中，我们采用了基于线性预测编码（LPC）和Volterra滤波器的组合的脑电图（EEG）信号的非线性噪声估计方法，这是eEG信号中估计噪声的新的和良好方法。我们最初使用LPC滤波器来估计EEG信号中存在的噪声（相关和不相关噪声）加上信号的不相关部分（其信号的一部分与其过去的样本没有线性关系）。之后，我们使用非线性Volterra模型来估计EEG信号中的现有噪声（相关和不相关的噪声）。我们表明，通过采用LPC和Volterra滤波器的级联，我们可以大大提高EEG信号中的信噪比（SNR）的比例至少为1.94。此外，我们将模拟结果与我们使用的volterra滤波器进行了比较。与volterra滤波器相比，我们的SNR有显着增加。

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