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Neural Computing and Applications

, Volume 28, Issue 11, pp 3153–3161 | Cite as

Combined feature extraction method for classification of EEG signals

  • Yong Zhang
  • Xiaomin Ji
  • Bo Liu
  • Dan Huang
  • Fuding Xie
  • Yuting Zhang
Original Article

Abstract

Classification of electroencephalogram (EEG) signals is an important task in brain–computer interfaces applications. This paper combines autoregressive (AR) model and sample entropy and presents a combination strategy of feature extraction. Each feature vector obtained from the combination strategy contains two parts: AR coefficients and sample entropy values. In the classification phase, this paper employs support vector machine (SVM) with RBF kernel as the classifier. The proposed method is used in the five mental task experiments. Experimental results show that the SVM classifier performs very well in classifying EEG signals using the combination strategy of feature extraction. It obtains a better accuracy in comparison with AR-based method. The results also indicate that the combination strategy of AR model and sample entropy can effectively improve the classification performance of EEG signals.

Keywords

EEG signal Feature extraction Autoregressive model Sample entropy Support vector classification Brain–computer interfaces 

Notes

Acknowledgments

This work is partly supported by National Natural Science Foundation of China (No. 61373127) and the University Scientific Research Project of Liaoning Education Department of China (No. 2011186).

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Yong Zhang
    • 1
    • 2
  • Xiaomin Ji
    • 1
  • Bo Liu
    • 1
  • Dan Huang
    • 1
  • Fuding Xie
    • 3
  • Yuting Zhang
    • 1
  1. 1.School of Computer and Information TechnologyLiaoning Normal UniversityDalianChina
  2. 2.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  3. 3.School of Urban and Environmental ScienceLiaoning Normal UniversityDalianChina

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