Study on Classification of EEG Signals Based on Wavelet Transformation and BP Neural Network

DOI

10.2991/ameii-15.2015.52How to use a DOI?

Keywords

Electroencephalography Signal; Wavelet Transformation; BP Neural Network

Abstract

The method of wavelet transformation and BP neural network for classification of epileptic intermission and epileptic attack signals was studied. The discrete binary wavelet transformation of EEG signal was carried out with db4 wavelet function. The third to fifth level signals by reconstruction and noise reduction were extracted as feature signals. The BP neural network with three levels was built for signal classification by the variation coefficient and fluctuation index. The accuracy recognition rate of EEG signals reaches 95% in simulation experiment. The results show that the method on classification of epileptic intermission and epileptic attack signals is effective and rapid.

Copyright

© 2015, 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  - Zhulin Yu
AU  - Bing Zhao
AU  - Jie Liu
AU  - Mingtao Yu
AU  - Ling Xu
PY  - 2015/04
DA  - 2015/04
TI  - Study on Classification of EEG Signals Based on Wavelet Transformation and BP Neural Network
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 285
EP  - 289
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.52
DO  - 10.2991/ameii-15.2015.52
ID  - Yu2015/04
ER  -