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Supervised Relevance Analysis for Multiple Stein Kernels for Spatio-Spectral Component Selection in BCI Discrimination Tasks

  • Camilo López-Montes
  • David Cárdenas-PeñaEmail author
  • G. Castellanos-Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

Abstract

Brain-Computer Interfaces (BCI) allow direct communication from the human brain to machines by analyzing sensorimotor activity. Within the most common paradigms of cognitive neuroscience for BCI, the Motor Imagery one relies on imaging movement of hands, feet, and tongue, usually. Standard BCI systems make use of electroencephalographic signals due to its high temporal resolution, portability, and easiness to implement. Recently, the filter-banked analysis of common spatial patterns has proved to be among the most discriminative approaches. Nonetheless, such an approach results in high dimensional spaces yielding to overtrained systems, or ill-posed spatial covariance matrices, when the number of EEG sensors increases. In this work, we propose a spatio-spectral relevance analysis, termed Multiple Stein Kernels (MSK), to simultaneously select channels and bands in a supervised criterion, so reducing the spatial and frequency dimension. We test our approach in three known BCI datasets with a varying number of subjects and channels. The attained classification results prove that MKS not only improves the classification accuracy and reduces the dimension of the representation space, but also select frequency bands that are physiologically interpretable.

Notes

Acknowledgment

This work was developed for the research project 111974454238 funded by Colciencias.

References

  1. 1.
    Cho, H., Ahn, M., Ahn, S., Kwon, M., Jun, S.C.: EEG datasets for motor imagery brain-computer interface. GigaScience 6(7), 1–8 (2017)CrossRefGoogle Scholar
  2. 2.
    Huang, G., Liu, G., Zhang, D., Zhu, X.: Model based generalization analysis of common spatial pattern in brain computer interfaces. Cogn. Neurodyn. 4(3), 217–223 (2010)CrossRefGoogle Scholar
  3. 3.
    Nguyen, C.H., Artemiadis, P.: EEG feature descriptors and discriminant analysis under Riemannian manifold perspective. Neurocomputing 275, 1871–1883 (2018)CrossRefGoogle Scholar
  4. 4.
    Ortiz-Rosario, A., Adeli, H.: Brain-computer interface technologies: from signal to action. Rev. Neurosci. 24(5), 537–552 (2013)CrossRefGoogle Scholar
  5. 5.
    Yger, F., Berar, M., Lotte, F.: Riemannian approaches in brain-computer interfaces: a review. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1753–1762 (2017)CrossRefGoogle Scholar
  6. 6.
    Yger, F., Lotte, F., Sugiyama, M.: Averaging covariance matrices for EEG signal classification based on the CSP: an empirical study. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2721–2725 (2015)Google Scholar
  7. 7.
    Zhang, Y., Wang, Y., Jin, J., Wang, X.: Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification. Int. J. Neural Syst. 27(02), 1650032 (2017)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., Zhou, G., Jin, J., Wang, X., Cichocki, A.: Optimizing spatial patterns with sparse filter bands for motor-imagery based brain-computer interface. J. Neurosci. Methods 255, 85–91 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Camilo López-Montes
    • 1
  • David Cárdenas-Peña
    • 2
    Email author
  • G. Castellanos-Dominguez
    • 1
  1. 1.Signal Processing and Recognition GroupUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Automatic Research GroupUniversidad Tecnológica de PereiraPereiraColombia

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