EEG signal denoising based on wavelet transform

被引：0

作者：

Jia Xi ^{[1
]}

Gao Zhenbin ^{[1
]}

机构：

[1] Hebei Univ Technol, Tianjin 300130, Peoples R China

来源：

Proceedings of the First International Symposium on Test Automation & Instrumentation, Vols 1 - 3 | 2006年

关键词：

wavelet transform; BCI; EEG; de-noising;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

It is necessary to increase the feature extraction capability of the classifier by applying wavelet transformation to the EEG signals. In this paper the use of wavelet transformation as a preprocessing tool for EEG signal de-noising is examined and a novel threshold is proposed for wavelet threshold de-noising method. First, hard threshold, soft threshold and the proposed threshold are used for de-noising and decomposing of the EEG data. And then the wavelet coefficient is used as extracted feature set and is fed to a probabilistic neural network classifier to organize the EEG signals into different activities. At last the effectiveness of de-noising with different thresholds is examined by the classification result. It is shown that the novel threshold is better than the last two thresholds for EEG signal de-noising based on wavelet transform.

引用

页码：506 / 509

页数：4