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Neural Computing and Applications
Published in: Neural Computing and Applications 5/2015

01-07-2015 | Original Article

Comparison of classification methods on EEG signals based on wavelet packet decomposition

Authors: Yong Zhang, Yuting Zhang, Jianying Wang, Xiaowei Zheng

Published in: Neural Computing and Applications | Issue 5/2015

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Abstract

EEG signals play an important role in both the diagnosis of neurological diseases and understanding the psychophysiological processes. Classification of EEG signals 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 combination strategy of sample entropy and extreme learning machine has shown great performance, which obtains good classification accuracy and low training time.
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Metadata
Title
Comparison of classification methods on EEG signals based on wavelet packet decomposition
Authors
Yong Zhang
Yuting Zhang
Jianying Wang
Xiaowei Zheng
Publication date
01-07-2015
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2015
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1786-7

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