Skip to main content
SpringerLink
Log in

ELM_Kernel and Wavelet Packet Decomposition Based EEG Classification Algorithm

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Rehabilitation technology based on brain-computer interface (BCI) has become a promising approach for patients with dyskinesia to regain movement. In this paper, a novel classification algorithm is proposed based on the characteristic of electroencephalogram (EEG) signals. Specifically wavelet packet decomposition (WPD) and Extreme learning machine with kernel (ELM_Kernel) algorithm are studied. In view of the existence of cross-banding of WPD, the average energy of the wavelet packets of the corresponding frequency bands which belong to the mu and beta rhythm are used to form the feature vectors that are classified by the ELM_Kernel algorithm. Simulation results demonstrate that the proposed algorithm produces a high probability of correct classification of 97.8% and outperforms state-of-the-art algorithms such as ELM, BP and SVM in terms of both training time and classification accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

REFERENCES

  1. Vaughan, T.M. and Wolpaw, J.R., The third international meeting on brain-computer interface technology: Making a difference, IEEE Trans. Neural Syst. Rehabil. Eng., 2006, vol. 14, pp. 126–127.

    Article  Google Scholar 

  2. Ming, D., Wang, K., He, F., Qi, H.Z., and Wan, B.K., Study on physiological information detection and application evoked by motor imagery: Review and prospect, Chin. J. Sci. Instrum., 2014, vol. 35, no. 9, pp. 1921–1931.

    Google Scholar 

  3. Pfurtscheller, G., Putz, G.R.M., Schlogl, A., Graimann, B., Scherer, R., Leeb, R., Brunner, C., Keinrath, C., Lee, F., Townsend, G., Vidaurre, C., and Neuper, C., 15 Years of BCI research at Graz University of Technology: Current projects, IEEE Trans. Neural Syst. Rehabil. Eng., 2006, vol. 14, no. 2, pp. 205–210.

    Article  Google Scholar 

  4. Lin, L. and Wei, Q.G., Application of CSSD and AAR in EEG signal processing, Commun. Technol., 2009, vol. 42, no. 2, pp. 207–210.

    Google Scholar 

  5. Liu, C., Zhao, H.B., Li, C.S., and Wang, H., CSP/SVM-based EEG classification of imagined hand movements, J. Northeast. Univ.(Nat. Sci.), 2010, vol. 31, no. 8, pp. 1098–1101.

    Google Scholar 

  6. Yang, Y., Si, Y.J., and Song, X.Y., ST segment classification of ECG signals based on wavelet transform and support vector machine, J. Jilin Univ. (Inf. Sci. Ed.), 2016, vol. 34, no. 3, pp. 315–319.

  7. Yan, S.Y., Liu, C., Zhao, H.B., and Wang, H., Imaginary EEG feature extraction based on wavelet packet decomposition, Chin. J. Sci. Instrum., 2012, vol. 33, no. 8, pp. 1748–1752.

    Google Scholar 

  8. Li, M.A., Lin, L., and Yang, J.F., Adaptive feature extraction based on best on best basis of wavelet packet for motor imagery EEG, Comput. Measur. Control, 2011, vol. 19, no. 11, pp. 2755–2758, 2762.

  9. Wang, D., Miao, D.Q., and Wang, R.Z., A new method of EEG classification with feature extraction based on wavelet packet decomposition, Acta Electron. Sin., 2013, vol. 41, no. 1, pp. 193–198.

    Article  Google Scholar 

  10. Wang, S.Y., Zhu, G.X., and Tang, Y.Y., Feature extraction using best wavelet packet transform, Acta Electron. Sin., 2003, vol. 31, no. 7, pp. 1035–1038.

    Google Scholar 

  11. Huang, G.B., Zhu, Q.Y., and Siew, C.K., Extreme learning machine: Theory and applications, Neurocomputing, 2006, vol. 70, pp. 489–501.

    Article  Google Scholar 

  12. Huang, G.B., Zhou, H.M., Ding, X.J., and Zhang, R., Extreme learning machine for regression and multiclass classification, IEEE Trans. Syst. Man Cybern.,Part B (Cyben.), 2012, vol. 42, no. 2, pp. 513–529.

    Google Scholar 

  13. Liang, N.Y., Saratchandran, P., Huang, G.B., and Sundararajan, N., Classification of mental tasks from EEG signals using extreme learning machine, Int. J. Neural Syst., 2006, vol. 16, no. 1, pp. 29–38.

    Article  Google Scholar 

  14. Yuan, Q., Zhou, W.D., Li, S.F., and Cai, D.M., Approach of EEG detection based on ELM and approximate entropy, Chin. J. Sci. Instrum., 2012, vol. 33, no. 3, pp. 514–519.

    Google Scholar 

  15. Huang, Z.Y., Yu, Y.L., Gu, J., and Liu, H.P., An efficient method for traffic sign recognition based on extreme learning machine, IEEE Trans. Cybern., 2016, no. 99, pp. 1–14.

Download references

Funding

This work was supported by The National Key Research and Development Program of China [2017YFB1304101], Beijing Natural Science Foundation [L172050], Hebei provincial major S&T Research and Development Projects [18277610D], The Fundamental Research Funds for Central Public Welfare Research Institutes [118 009 001 000 160 001], Key Laboratory of Rehabilitation Aids Technology and System of the Ministry of Civil Affairs and Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability.

Author information

Authors and Affiliations

  1. National Research Center for Rehabilitation Technical Aids, BDA, China

    Li Wang, Zhi Lan, Qiang Wang, Rong Yang & Hongliang Li

  2. Qinhuangdao Institute of National Research Center for Rehabilitation Technical Aids, Funing Economic and Technological Development Zone, Qinhuangdao, China

    Li Wang

Authors
  1. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hongliang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Wang.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li Wang, Lan, Z., Wang, Q. et al. ELM_Kernel and Wavelet Packet Decomposition Based EEG Classification Algorithm. Aut. Control Comp. Sci. 53, 452–460 (2019). https://doi.org/10.3103/S0146411619050079

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411619050079

Keywords:

Search

Navigation