一种新的相干性估计方法:平滑最小方差无失真响应的幅度平方相干性

D. Cui, Juan Wang, Zhaohui Li, Xiaoli Li
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引用次数: 4

摘要

相干幅度平方(magnitude squared coherence, MSC)是计算神经信号间连通性的一种重要方法。它提供了一个更好的光谱分辨率比韦尔奇的方法,经常用于分析脑电图(EEG)同步活动。最小方差无失真响应(MVDR)是一种基于匹配滤波器组理论的频谱估计方法。cherieet - belouchrani (CB)核用于测量时频分布信号的能量,该核具有显著的抗干扰性并保持高分辨率测量值。将MVDR光谱与CB核相结合,提出了一种用CB核对MVDR进行平滑处理的相干度平方估计方法(SMVDR)。仿真结果表明,SMVDR MSC方法比MVDR MSC方法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new coherence estimating method: The magnitude squared coherence of smoothing minimum variance distortionless response
The magnitude squared coherence (MSC) is an important method to calculate the connectivity between neural signals. It provides a better spectral resolution than the Welch's method and is often used in analyzing electroencephalograph (EEG) synchronization activity. The minimum variance distortionless response (MVDR) is a spectral estimation method based on matched filterbank theory. The Cheriet-Belouchrani (CB) kernel is provided for measuring the energy of a signal in time-frequency distribution, which has significant interference mitigation and preserves high resolution measure values. By combining MVDR spectra and CB kernel, a new magnitude squared coherence estimating method is proposed in the paper by smoothing the MVDR with the CB kernel (SMVDR). The simulation results show that SMVDR MSC approach has better performances than the MVDR MSC method.
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