Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces

Javier Asensio-Cubero, John Q. Gan, Ramaswamy Palaniappan

Abstract

Brain computer interfaces are control systems that allow the interaction with electronic devices by analysing the user’s brain activity. The analysis of brain signals, more concretely, electroencephalographic data, represents a big challenge due to its noisy and low amplitude nature. Many researchers in the field have applied wavelet transform in order to leverage the signal analysis benefiting from its temporal and spectral capabilities. In this study we make use of the so-called second generation wavelets to extract features from temporal, spatial and spectral domains. The complete multiresolution analysis operates over an enhanced graph representation of motor imaginary trials, which uses per-subject knowledge to optimise the spatial links among the electrodes and to improve the filter design. As a result we obtain a novel method that improves the performance of classifying different imaginary limb movements without compromising the low computational resources used by lifting transform over graphs.

References

  1. Allison, B., Graimann, B., and Grser, A. (2008). Why use a BCI if you are healthy. Proceedings of BRAINPLAY, playing with your brain.
  2. Asensio-Cubero, J., Gan, J. Q., and Palaniappan, R. (2013). Multiresolution analysis over simple graphs for brain computer interfaces. Journal of Neural Engineering, 10(4):046014.
  3. Blankertz, B., Muller, K. R., Krusienski, D. J., Schalk, G., Wolpaw, J. R., Schlogl, A., Pfurtscheller, G., Millan, J. R., Schroder, M., and Birbaumer, N. (2006). The BCI competition III: validating alternative approaches to actual BCI problems. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14(2):153- 159.
  4. Brunner, C., Leeb, R., Muller-Putz, G. R., Schlogl, A., and Pfurtscheller, G. (2008). BCI competition 2008 - Graz data set A. http://www.bbci.de/competition/iv/desc 2a.pdf.
  5. Carrera-Leon, O., Ramirez, J. M., Alarcon-Aquino, V., Baker, M., D'Croz-Baron, D., and Gomez-Gil, P. (2012). A motor imagery bci experiment using wavelet analysis and spatial patterns feature extraction. In Engineering Applications (WEA), 2012 Workshop on, pages 1-6. IEEE.
  6. Claypoole Jr, R. L., Baraniuk, R. G., and Nowak, R. D. (1998). Adaptive wavelet transforms via lifting. In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, volume 3, pages 1513-1516.
  7. Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1):37-46.
  8. Cover, T. M. and Thomas, J. A. (2012). Elements of Information Theory. John Wiley & Sons.
  9. Daubechies, I. (2006). Ten lectures on wavelets. Society for industrial and applied mathematics.
  10. Dornhege, G. (2007). Toward Brain-Computer Interfacing. The MIT Press.
  11. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence., 11(7):674-693.
  12. Martinez-Enriquez, E. and Ortega, A. (2011). Lifting transforms on graphs for video coding. In Data Compression Conference, pages 73-82. IEEE.
  13. Narang, S. K. and Ortega, A. (2009). Lifting based wavelet transforms on graphs. In Conference of Asia-Pacific Signal and Information Processing Association, pages 441-444.
  14. Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information criteria of maxdependency, max-relevance, and min-redundancy. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(8):1226-1238.
  15. Perseh, B. and Sharafat, A. R. (2012). An efficient p300- based bci using wavelet features and ibpso-based channel selection. Journal of Medical Signals and Sensors, 2(3):128.
  16. Pfurtscheller, G. and Lopes da Silva, F. H. (1999). Eventrelated EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology, 110(11):1842-1857.
  17. Ramoser, H., Muller-Gerking, J., and Pfurtscheller, G. (2000). Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Transactions on Rehabilitation Engineering, 8(4):441-446.
  18. Schrder, P. and Sweldens, W. (1995). Spherical wavelets: Efficiently representing functions on the sphere. In Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pages 161-172. ACM.
  19. Shen, G. and Ortega, A. (2008). Comopact image representation using wavelet lifting along arbitrary trees. In 15th IEEE International Conference on Image Processing, 2008. ICIP 2008., pages 2808-2811. IEEE.
  20. Sweldens, W. (1996). Wavelets and the lifting scheme: A 5 minute tour. Zeitschrift fur Angewandte Mathematik und Mechanik, 76(2):41-44.
  21. Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets. SIAM Journal on Mathematical Analysis, 29(2):511.
  22. Sweldens, W. and Schrder, P. (2000). Building your own wavelets at home. Wavelets in the Geosciences, pages 72-107.
Download


Paper Citation


in Harvard Style

Asensio-Cubero J., Q. Gan J. and Palaniappan R. (2014). Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces . In Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-006-2, pages 5-12. DOI: 10.5220/0004704200050012


in Bibtex Style

@conference{phycs14,
author={Javier Asensio-Cubero and John Q. Gan and Ramaswamy Palaniappan},
title={Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces},
booktitle={Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2014},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004704200050012},
isbn={978-989-758-006-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces
SN - 978-989-758-006-2
AU - Asensio-Cubero J.
AU - Q. Gan J.
AU - Palaniappan R.
PY - 2014
SP - 5
EP - 12
DO - 10.5220/0004704200050012