EEG signal classification with feature selection based on one-dimension real valued particle swarm optimization

DOI

10.2991/mce-14.2014.72How to use a DOI?

Keywords

EEG signals; wavelet packet decomposition; approximation entropy; feature selection; particle swarm optimization

Abstract

In this study, a new scheme was presented for the EEG signal classification with feature selection based on one-dimension real valued particle swarm optimization. In the proposed scheme, normal and abnormal EEG signals were decomposed into various frequency bands with one fourth-level wavelet packet decomposition. Approximation entropy value of the wavelet coefficients at all nodes of the decomposition tree were used as a feature set to characterize the predictability of the EEG data within the corresponding frequency bands. Then, the one-dimension real valued particle swarm optimization algorithm was used to find the optimal feature subset by maximizing the classification performance of a support vector machine based EEG signal classifier. Experimental results showed that the proposed method improved the classification performance substantially and got a much less size of optimal feature subset with compared to the other methods.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jun Wang
AU  - Yan Zhao
PY  - 2014/03
DA  - 2014/03
TI  - EEG signal classification with feature selection based on one-dimension real valued particle swarm optimization
BT  - Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 326
EP  - 330
SN  - 1951-6851
UR  - https://doi.org/10.2991/mce-14.2014.72
DO  - 10.2991/mce-14.2014.72
ID  - Wang2014/03
ER  -