Feature Extraction and Classification of sEMG Based on ICA and EMD Decomposition of AR Model

被引：0

作者：

Shang Xiaojing ^{[1
]}

Tian Yantao ^{[2
]}

Li Yang ^{[1
,3
]}

机构：

[1] Jilin Univ, Sch Commun Engn, Changchun 130023, Peoples R China

[2] Minist Educ Jilin Univ, Sch Commun Engn, Lab Bion Engn, Changchun, Peoples R China

[3] Jilin Univ, Sch Commun Engn, Changchun, Peoples R China

来源：

2011 INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND CONTROL (ICECC) | 2011年

关键词：

sEMG; Independent component analysis; Empirical mode decomposition (EMD); Probabilistic neural networks; Pattern recognition;

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：

The surface EMG (sEMG) is a biological electrical signal of neuromuscular activity distribution. From the point of the non-stationary and nonlinear, the independent component analysis method is firstly used to eliminate the power frequency interference in sEMG. Secondly, the low noise signal is processed by empirical mode decomposition (EMD), then use the decomposed signal to establish AR model. The model coefficients are used as signal features and PNN optimized by particle swarm optimization (PSO) is used to classify six types of forearm motions. The experimental results demonstrate the effectiveness of the proposed method.

引用

页码：1464 / 1467

页数：4

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