Advertisement

Frequency Recognition Based on Wavelet-Independent Component Analysis for SSVEP-Based BCIs

  • Limin Yang
  • Ze Wang
  • Chi Man Wong
  • Feng Wan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

Among the EEG-based BCIs, SSVEP-based BCIs have gained much attention due to the advantages of relatively high information transfer rate (ITR) and short calibration time. Although in SSVEP-based BCIs the frequency recognition methods using multiple channels EEG signals may provide better accuracy, using single channel would be preferable in a practical scenario since it can make the system simple and easy-to-use. To this goal, we propose a new single channel method based on wavelet-independent component analysis (WICA) in the SSVEP-based BCI, in which wavelet transform (WT) is applied to decompose a single channel signal into several wavelet components and then independent component analysis (ICA) is applied to separate the independent sources from the wavelet components. Experimental results show that most of the time the recognition accuracy of the proposed single channel method is higher than the conventional single channel method, power spectrum (PS) method.

Keywords

wavelet-independent component analysis (WICA) SSVEP frequency recognition BCI 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bin, G., Gao, X., Yan, Z., Hong, B., Gao, S.: An Online Multi-Channel SSVEP-Based Brain–Computer Interface Using a Canonical Correlation Analysis Method. J. Neural Eng. 6, 046002 (2009)CrossRefGoogle Scholar
  2. 2.
    Guger, C., Allison, B.Z., Groβwindhager, B., Prückl, R., Hintermüller, C., Kapeller, C., Bruckner, M., Krausz, G., Edlinger, G.: How Many People Could Use an SSVEP BCI? Front Neurosci. 6 (2012)Google Scholar
  3. 3.
    Wu, Z., Yao, D.: Frequency Detection with Stability Coefficient for Steady-State Visual Evoked Potential (SSVEP)-Based BCIs. J. Neural Eng. 5, 36–43 (2008)CrossRefGoogle Scholar
  4. 4.
    Wu, Z.: SSVEP Extraction Based on the Similarity of Background EEG. PloS One 9, e93884 (2014)CrossRefGoogle Scholar
  5. 5.
    Lopez, M., Pelayo, A., Madrid, F., Prieto, E., Statistical Characterization, A.: of Steady-State Visual Evoked Potentials and Their Use in Brain–Computer Interfaces. Neural Processing Lett. 29, 179–187 (2009)CrossRefGoogle Scholar
  6. 6.
    Nan, W., Wong, C.M., Wang, B., Wan, F., Mak, P.U., Mak, P.I., Vai, M.I.: A Comparison of Minimum Energy Combination and Canonical Correlation Analysis for SSVEP Detection. In: 5th International IEEE/EMBS Conference on Neural Engineering, pp. 469–472. IEEE Press, Cancun (2011)Google Scholar
  7. 7.
    Lin, J., Zhang, A.: Fault Feature Separation Using Wavelet-ICA Filter. NDT&E Int. 38, 421–427 (2005)CrossRefGoogle Scholar
  8. 8.
    Comon, P.: Independent Component Analysis: a New Concept? Signal Processing 36, 287–314 (1994)CrossRefGoogle Scholar
  9. 9.
    Nason, G.P., Silverman, B.W.: The Stationary Wavelet Transform and Some Statistical Applications. Wavelets and Statistics 103, 281–299 (1995)CrossRefGoogle Scholar
  10. 10.
    Kirkove, M., Francois, C., Verly, J.: Comparative Evaluation of Existing and New Methods for Correcting Ocular Artifacts in Electroencephalographic Recordings. Signal Processing 98, 102–120 (2014)CrossRefGoogle Scholar
  11. 11.
    Sheoran, M., Kumar, S., Kumar, A.: Wavelet-ICA Based Denoising of Electroencephalogram Signal. Int. J. Inform. Comput. Technol. 4, 1205–1210 (2014)Google Scholar
  12. 12.
    Hyvarinen, A., Karhunen, J., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comput. 9, 1483–1492 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. </SimplePara> <SimplePara>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  • Limin Yang
    • 1
  • Ze Wang
    • 1
  • Chi Man Wong
    • 1
  • Feng Wan
    • 1
  1. 1.Department of Electrical and Computer Engineering, Faculty of Science and TechnologyUniversity of MacauMacauChina

Personalised recommendations