Single Trial Classification for Mobile BCI - A Multiway Kernel Approach

Lieven Billiet, Borbála Hunyadi, Vladimir Matic, Sabine Van Huffel, Michel Verleysen, Maarten De Vos

Abstract

Subspace methods have been applied in various application fields to obtain robust results. Using multilinear algebra, they can also be applied on structured tensorial data. This work combines this principle with the power of non-linear kernels to investigate its merits in single trial classification for a mobile BCI ERP classification task. The accuracy difference with regard to more conventional vector kernels is evaluated for sitting and walking condition, increasing training data set and averaging over multiple trials. The study concludes that in general, the tensorial approach does not yield any advantage, though it might for specific subjects.

References

  1. Cristianini, N., Kandola, J., Elisseeff, A., and ShaweTaylor, J. (2002). On kernel-target alignment. In Advances in Neural Information Processing Systems 14, pages 367-373. MIT Press.
  2. De Lathauwer, L., De Moor, B., and Vandewalle, J. (2000). A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl., 21(4):1253-1278.
  3. De Vos, M., Gandras, K., and Debener, S. (2013). Towards a truly mobile auditory brain-computer interface: Exploring the P300 to take away. International Journal of Psychophysiology.
  4. Debener, S., Minow, F., Emkes, R., Gandras, K., and de Vos, M. (2012). How about taking a low-cost, small, and wireless eeg for a walk? Psychophysiology, 49(11):1617-1621.
  5. Gabor, D. (1946). Theory of Communication. Journal of the Institution of Electrical Engineers, 93(26):429-457.
  6. Halder, S., Rea, M., Andreoni, R., Nijboer, F., Hammer, E. M., Kleih, S. C., Birbaumer, N., and Kuebler, A. (2010). An auditory oddball brain-computer interface for binary choices. Clinical Neurophysiology, 121:516-523.
  7. Hansen, P. C. and Jensen, S. H. (1998). FIR filter representations of reduced-rank noise reduction. IEEE Transactions on Signal Processing, 46(6):1737-1741.
  8. Hunyadi, B., Signoretto, M., Debener, S., Huffel, S. V., and Vos, M. D. (2013). Classification of structured EEG Tensors using Nuclear Norm Regularization: Improving P300 Classification. In International Workshop on Pattern Recognition in Neuroimaging (PRNI), pages 98-101. IEEE.
  9. Jackson, M. M. and Mappus, R. (2010). Applications for Brain-Computer Interfaces. In Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction, chapter 1. Springer.
  10. Kung, S. Y., Arun, K. S., and Rao, D. V. B. (1983). State-space and singular-value decomposition-based approximation methods for the harmonic retrieval problem. J. Opt. Soc. Am., 73(12):1799-1811.
  11. Li, J. and Zhang, L. (2010). Regularized tensor discriminant analysis for single trial EEG classification in bci. Pattern Recognition Letters, 31:619-628.
  12. Liu, H. and Motoda, H., editors (2008). Computational Methods of Feature Selection. Chapman & Hall.
  13. Mallat, S. and Zhang, Z. (1993). Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41:3397-3415.
  14. Nasehi, S. and Pourghassem, H. (2011). Real-Time Seizure Detection based on EEG and ECG Fused Features using Gabor Functions. International Conference on Intelligent Computation and Bio-Medical Instrumentation, 0:204-207.
  15. Onishi, A., Phan, A. H., Matsuoka, K., and Cichocki, A. (2012). Tensor classification for P300-based brain computer interface. In ICASSP, pages 581-584. IEEE.
  16. Signoretto, M. (2011). Kernels and Tensors for Structured Data Modelling. PhD thesis, KULeuven.
  17. Suykens, J. A. K. and Vandewalle, J. (1999). Least Squares Support Vector Machine Classifiers. Neural Process. Lett., 9(3):293-300.
  18. Wexler, J. and Raz, S. (1990). Discrete Gabor Expansions. Signal Processing, 21(3):207-220.
  19. Zhao, Q., Zhou, G., Adali, T., Zhang, L., and Cichocki, A. (2013). Kernelization of Tensor-Based Models for Multiway Data Analysis. IEEE Signal Processing Magazine, 30(4):137-148.
Download


Paper Citation


in Harvard Style

Billiet L., Hunyadi B., Matic V., Van Huffel S., Verleysen M. and De Vos M. (2015). Single Trial Classification for Mobile BCI - A Multiway Kernel Approach . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 5-11. DOI: 10.5220/0005163000050011


in Bibtex Style

@conference{biosignals15,
author={Lieven Billiet and Borbála Hunyadi and Vladimir Matic and Sabine Van Huffel and Michel Verleysen and Maarten De Vos},
title={Single Trial Classification for Mobile BCI - A Multiway Kernel Approach},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={5-11},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005163000050011},
isbn={978-989-758-069-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Single Trial Classification for Mobile BCI - A Multiway Kernel Approach
SN - 978-989-758-069-7
AU - Billiet L.
AU - Hunyadi B.
AU - Matic V.
AU - Van Huffel S.
AU - Verleysen M.
AU - De Vos M.
PY - 2015
SP - 5
EP - 11
DO - 10.5220/0005163000050011