Identification and Classification of Electroencephalogram Signals Based on Independent Component Analysis

被引：2
|
作者：
Zhang, Chao [1 ]
Xu, Jing [1 ]
Pan, Su [2 ,3 ]
Yang, Yudan
机构：
[1] Changchun Univ, Changchun 130022, Jilin, Peoples R China
[2] Jilin Univ, Hosp 2, Dept Orthoped, Changchun 130041, Jilin, Peoples R China
[3] Jilin Univ, China Japan Union Hosp, Sci Res Ctr, Changchun 130033, Jilin, Peoples R China
来源：
NEUROQUANTOLOGY | 2018年 / 16卷 / 05期
关键词：
Electroencephalogram (EEG); Brain Computer Interface (BCI); Independent Component Analysis (ICA); Support Vector Machine (SVM);
D O I：
10.14704/nq.2018.16.5.1392
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
This paper aims to develop a desirable EEG-based classification algorithm. For this purpose, the discrete wavelet transform was applied to denoise the EEG signals. Then, the brain's left and right hand movement features were extracted from the denoised signals by the independent component analysis (ICA). Finally, the support vector machine (SVM) classifier was adopted to recognize and classify the movement of the left and right hand actions. The experimental results show that our method achieves the recognition accuracy of 89.5% and 90.6% respectively. The research findings provide a valuable reference for the future research into the BCI system.
引用
收藏
页码：832 / 838
页数：7
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