Denoising based on time-shift PCA
Denoising based on time-shift PCA
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DOI：
10.1016/j.jneumeth.2007.06.003
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发表时间：
2007-09-30
影响因子：
3
通讯作者：
Simon, Jonathan Z.
中科院分区：
文献类型：
--
作者：
de Cheveigne, Alain;Simon, Jonathan Z.
我们提出了一种从神经生理学记录（例如脑磁图（MEG））中消除环境噪声的算法。由参考磁力计测量的噪声场被最佳过滤并从脑通道中扣除。滤波器（每个参考[脑传感器对一个）是通过延迟参考信号、将它们正交化以获得基础、将脑传感器投影到源自噪声的基础上以及移除投影以获得干净的数据来获得的。合成数据的模拟表明大脑信号的失真是最小的。该方法通过为每个参考/大脑传感器对合成一个滤波器来补偿传感器之间的卷积失配，从而超越了以前的方法。该方法通过抑制有害噪声来提高健康和科学应用中记录的数据的价值，并减少对有害空间或光谱过滤的需要。它应该适用于更广泛的生理记录技术，例如脑电图、局部场电位等。 (c) 2007 Elsevier B.V. 保留所有权利。
We present an algorithm for removing environmental noise from neurophysiological recordings such as magnetoencephalography (MEG). Noise fields measured by reference magnetometers are optimally filtered and subtracted from brain channels. The filters (one per reference[brain sensor pair) are obtained by delaying the reference signals, orthogonalizing them to obtain a basis, projecting the brain sensors onto the noise-derived basis, and removing the projections to obtain clean data. Simulations with synthetic data suggest that distortion of brain signals is minimal. The method surpasses previous methods by synthesizing, for each reference/brain sensor pair, a filter that compensates for convolutive mismatches between sensors. The method enhances the value of data recorded in health and scientific applications by suppressing harmful noise, and reduces the need for deleterious spatial or spectral filtering. It should be applicable to a wider range of physiological recording techniques, such as EEG, local field potentials, etc. (c) 2007 Elsevier B.V. All rights reserved.