Fst-Filter: A flexible spatio-temporal filter for biomedical multichannel data denoising
Fst-Filter: A flexible spatio-temporal filter for biomedical multichannel data denoising
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DOI：
10.1109/embc.2015.7318965
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
Somchai Nuanprasert;Y. Adachi;Takashi Suzuki
中科院分区：
文献类型：
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作者：
Somchai Nuanprasert;Y. Adachi;Takashi Suzuki
在本文中，我们提出了一个多通道测量系统的降噪方法，其中真实的底层信号是空间低秩的，并且被空间相关噪声污染。我们提出的公式应用广义奇异值分解（GSVD）和信号恢复方法来扩展传统的基于子空间的方法来执行时空滤波。所实现的优化方案无需事先要求噪声协方差数据，用户可以从现有的多种高效时间滤波器中灵活选择满足不同时间噪声特性的去噪函数F（·）。在模拟脑磁图（MEG）实验中，与传统的主成分分析（PCA）、鲁棒主成分分析（RPCA）和多变量小波去噪（MWD）方法相比，该方法对脑源的估计精度更高。
In this paper, we present the noise reduction method for a multichannel measurement system where the true underlying signal is spatially low-rank and contaminated by spatially correlated noise. Our proposed formulation applies generalized singular value decomposition (GSVD) with signal recovery approach to extend the conventional subspace-based methods for performing the spatio-temporal filtering. Without necessarily requiring the noise covariance data in advance, the implemented optimization scheme allows users to choose the denoising function, F(·) flexibly satisfying for different temporal noise characteristics from a variety of existing efficient temporal filters. An effectiveness of proposed method is demonstrated by yielding the better accuracy for the brain source estimation on simulated magnetoencephalography (MEG) experiments than some traditional methods, e.g., principal component analysis (PCA), robust principal component analysis (RPCA) and multivariate wavelet denoising (MWD).