ORIGINAL ARTICLE
Comparison of classification methods on EEG signals based
on wavelet packet decomposition
Yong Zhang
•
Yuting Zhang
•
Jianying Wang
•
Xiaowei Zheng
Received: 17 September 2014 / Accepted: 9 December 2014 / Published online: 24 December 2014
The Natural Computing Applications Forum 2014
Abstract EEG signals play an important role in both the
diagnosis of neurological diseases and understanding the
psychophysiological processes. Classification of EEG sig-
nals includes feature extraction and feature classification.
This paper uses approximate entropy and sample entropy
based on wavelet package decomposition as the feature
exaction methods and employs support vector machine and
extreme learning machine as the classifiers. Experiments
are performed in epileptic EEG data and five mental tasks,
respectively. Experimental results show that the combina-
tion strategy of sample entropy and extreme learning
machine has shown great performance, which obtains good
classification accuracy and low training time.
Keywords EEG Wavelet packet Classification
Approximate entropy Extreme learning machine
1 Introduction
The electroencephalogram (EEG) signals play an important
role in both the diagnosis of neurological diseases and under-
standing the psychophysiological processes. Although several
methods for the brain function analysis such as
magnetoencephalography (MEG), functional magnetic reso-
nance imaging (fMRI), and positron emission tomography
(PET) have been introduced, the EEG signal is still a valuable
tool for monitoring the brain activity due to its relatively low
cost and being convenient for the patient [1]. Effectively
classifying EEG is the foundation for building usable Brain–
Computer Interfaces (BCI) and improving the performance of
EEG analysis software used in clinical and research settings.
Feature extraction and classification of EEG signals is a
challenge for engineers and scientists. In recent years, there
has been an increasing interest in the application of
machine learning methods for EEG signal. Many methods
have been proposed to classify and analyze EEG signal.
Among these methods, support vector machine (SVM) and
artificial neural network [2, 3] are two widely used clas-
sification techniques. Most of EEG signal classifications
are built by time–frequenc y domain-based feature extrac-
tion followed by a variety of classification models. So, the
classification performance of these methods depends on the
feature extraction of the EEG signal.
Wavelet has played an important role in biomedical
signal proce ssing for its ability to capture localized spatial-
frequency information of EEG signals [4]. In the feature
extraction phase, this paper uses approximate entropy
(ApEn) and sample entropy (SampEn) based on wavelet
packet decomposition (WPD). Obtained feature values are
employed as the inputs of a classifier, such as extreme
learning machine (ELM) and SVM. Experiments are per-
formed in four different combinations of feature extrac-
tions and classifiers. Experimental results in epileptic EEG
data and five mental tasks indicate that the combination of
sample entropy and ELM has shown great performance.
The remaining of the paper is organized as follows. In
Sect. 2, we briefly presented the literature survey that has
been performed. In Sect. 3, we described about the
Y. Zhang Y. Zhang X. Zheng (&)
School of Computer and Information Technology, Liaoning
Normal University, No. 1, Liushu South Street, Ganjingzi
District, Dalian, Liaoning Province 116081, China
e-mail: zhengxw_ln@163.com
Y. Zhang
e-mail: zhyong@lnnu.edu.cn
J. Wang
School of Psychology, Liaoning Normal University,
Dalian 116029, China
123
Neural Comput & Applic (2015) 26:1217–1225
DOI 10.1007/s00521-014-1786-7
剩余8页未读，继续阅读
资源评论
weixin_38631729
- 粉丝: 8
- 资源: 905
最新资源
- 峰会报告自动化生成基础教程
- 算法竞赛中的离散化 概念总结和基本操作全解
- 算法竞赛位运算（简单易懂）
- 常用一维二维 前缀和与差分算法模板总结
- SAR成像算法+后向投影（BP）算法+星载平台实测数据
- 横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横向循环焦点轮播图横
- 基于Java和HTML的留言墙、验证码、计算器基础项目设计源码
- 基于JAVA C/C++的嵌入式设备组网平台物联网框架设计源码
- 基于Java开发的高性能全文检索工具包jsearch设计源码
- 基于多语言技术的pt遨游助手手机版设计源码
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
