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An Algorithm Combining Spatial Filtering and Temporal Down-Sampling with Applications to ERP Feature Extraction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Event-related potentials (ERP) based brain-computer interfaces (BCI) is a promising technology for decoding mental states. Due to the high trail-to-trial variability and low signal-to-noise ratio caused by volume conduction, analyzing brain states corresponding to ERP on a single trial is a challenging task. In this paper, we propose a computationally efficient method for ERP feature extraction, termed spatial filtering and temporal down-sampling (SFTDS). The spatial filters and the temporal down-sampling weight vectors can be optimized under a single objective function by SFTDS. Experiments on real P300 data from 10 subjects show the superiority of SFTDS over other algorithms.

Keywords

ERP Spatial filter Weighted down-sampling Regularization 

Notes

Acknowledgments

This work was supported by 973 Program of China (No. 2015CB351703), the National Natural Science Foundation of China (No. 61403144, No. 61633010), the tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (No. 2015TQ01X361).

References

  1. 1.
    Zhang, Y., Zhao, Q., Jin, J., Wang, X., Cichocki, A.: A novel BCI based on ERP components sensitive to configural processing of human faces. J. Neural Eng. 9(2), 026018 (2012)CrossRefGoogle Scholar
  2. 2.
    Wolpaw, J., Birbaumer, N., McFarland, D., Pfutscheller, G., Vaughan, T.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)CrossRefGoogle Scholar
  3. 3.
    Koenig, T., Stein, M., Grieder, M., Kottlow, M.: A tutorial on data-driven methods for statistically assessing ERP topographies. Brain Topogr. 27(1), 72–83 (2014)CrossRefGoogle Scholar
  4. 4.
    Baillet, S., Mosher, J.C., Leahy, R.M.: Electromagnetic brain mapping. IEEE Sig. Process. Mag. 18, 14–30 (2001)CrossRefGoogle Scholar
  5. 5.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain-computer interfaces. J. Neural Eng. 4(2), R1 (2007)CrossRefGoogle Scholar
  6. 6.
    Luck, S.J.: An Introduction to the Event-Related Potential Technique. The MIT Press, Massachusetts (2005)Google Scholar
  7. 7.
    Chapman, R.M., McCrary, J.W.: EP component identification and measurement by principal components analysis. Brain Cogn. 27(3), 288–310 (1995)CrossRefGoogle Scholar
  8. 8.
    Makeig, S., Bell, A.J., Jung, T.P., Sejnowski, T.J.: Independent component analysis of electroencephalographic data. Adv. Neural Inf. Process. Syst. 8, 145–151 (1996)Google Scholar
  9. 9.
    Zibulevsky, M., Zeevi, Y.Y.: Extraction of a source from multichannel data using sparse decomposition. Neurocomputing 49(1), 163–173 (2002)CrossRefMATHGoogle Scholar
  10. 10.
    Makeig, S., Westerfield, M., Jung, T.P., Enghoff, S., Townsend, J., Courchesne, E., Sejnowski, T.J.: Dynamic brain sources of visual evoked responses. Science 295, 690–694 (2002)CrossRefGoogle Scholar
  11. 11.
    Morup, M., Hansen, L.K., Herrmann, C.S., Parnas, J., Arnfred, S.M.: Parallel factor analysis as an exploratory tool for wavelet transformed event-related EEG. NeuroImage 29(3), 938–947 (2006)CrossRefGoogle Scholar
  12. 12.
    Lee, H., Kim, Y.D., Cichocki, A.: Nonnegative tensor factorization for continuous EEG classification. Int. J. Neural Syst. 17, 305–317 (2007)CrossRefGoogle Scholar
  13. 13.
    Wu, W., Gao, S.: Learning event-related potentials (ERPs) from multichannel EEG recordings: a spatio-temporal modeling framework with a fast estimation algorithm. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6959–6962. IEEE Press, Boston (2011)Google Scholar
  14. 14.
    Wu, W., Wu, C., Gao, S., Liu, B., Li, Y., Gao, X.: Bayesian estimation of ERP components from multicondition and multichannel EEG. NeuroImage 88(1), 319–339 (2014)CrossRefGoogle Scholar
  15. 15.
    Liao, X., Yao, D., Wu, D., Li, C.: Combining spatial filters for the classification of single-trial EEG in a finger movement task. IEEE Trans. Biomed. Eng. 54(5), 821–831 (2007)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Zhou, G., Zhao, Q., Jin, J., Wang, X., Cichocki, A.: Spatial-temporal discriminant analysis for ERP-based brain-computer interface. IEEE Trans. Neural Syst. Rehabil. Eng. 21(2), 233–243 (2013)CrossRefGoogle Scholar
  17. 17.
    Krusienski, D.J., Sellers, E.W., McFarland, D.J., Vaughan, T.M., Wolpaw, J.R.: Toward enhanced P300 speller performance. J. Neurosci. Methods 167(1), 15–21 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Brain Computer Interfaces and Brain Information ProcessingSouth China University of TechnologyGuangzhouChina

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