Romo-Vazquez R, Ranta R, Louis-Dorr V, Maquin D
Centre de Recherche en Automatique de Nancy (CRAN-UMR 7039), Nancy-University, CNRS, ENSEM, 2 Avenue de la Forêt de Haye, Nancy, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5445-8. doi: 10.1109/IEMBS.2007.4353577.
The general framework of this research is the pre-processing of the electroencephalographic (EEG) signals. The goal of this paper is to compare several combinations of wavelet denoising (WD) and independent component analysis (ICA) algorithms for noise and artefacts removal. These methods are tested on simulated EEG, using different evaluation criteria. According to our results, the most effective method consists in source separation by SOBI-RO [1], followed by wavelet denoising by SURE thresholding [2].
本研究的总体框架是脑电图（EEG）信号的预处理。本文的目的是比较几种小波去噪（WD）和独立成分分析（ICA）算法组合，以去除噪声和伪迹。这些方法在模拟脑电图上进行测试，并使用不同的评估标准。根据我们的结果，最有效的方法是先通过SOBI-RO [1]进行源分离，然后通过Sure阈值化[2]进行小波去噪。
