EEG signal classification with feature selection based on one-dimension real valued particle swarm optimization
Jun Wang, Yan Zhao
Available Online March 2014.
- https://doi.org/10.2991/mce-14.2014.72How to use a DOI?
- EEG signals; wavelet packet decomposition; approximation entropy; feature selection; particle swarm optimization
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14) PB - Atlantis Press SP - 326 EP - 330 SN - 1951-6851 UR - https://doi.org/10.2991/mce-14.2014.72 DO - https://doi.org/10.2991/mce-14.2014.72 ID - Wang2014/03 ER -