Advertisement

Analysis of EEG Mapping Images to Differentiate Mental Tasks in Brain-Computer Interfaces

  • Andrés Úbeda
  • Eduardo Iáñez
  • José M. Azorín
  • Eduardo Fernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

This paper describes a study of a new classifier based on EEG mapping analysis in order to develop a Brain-Computer Interface (BCI) through computer vision techniques. To this end, the data from three different subjects (BCI Competition Data Set V) have been studied to show proper EEG maps of the three mental tasks registered. A new classifier based on image analysis of the EEG maps has been presented as a suitable way to distinguish between the different tasks, showing in which conditions of frequency and time the images obtained for each mental task can be best classified. The classifier has been tested obtaining the success percentage of classification of each subject showing that this kind of techniques are able to classify between three mental tasks with good results.

Keywords

Brain-Computer Interface (BCI) Electroencephalography (EEG) EEG Mapping Computer Vision 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gao, X., Dignfeng, X., Cheng, M., Gao, S.: A BCI-based Environmental Controller for the Motion-Disabled. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 137–140 (2003)CrossRefGoogle Scholar
  2. 2.
    Chapin, J.K., Moxon, K.A., Markowitz, R.S., Nicolelis, M.A.L.: Real-Time Control of a Robot Arm using Simultaneously Recorded Neurons in the Motor Cortex. Nature Neuroscience 2, 664–670 (1999)CrossRefGoogle Scholar
  3. 3.
    Serruya, M.D., Harsopoulos, N.G., Paninski, L., Fellows, M.R., Donoghue, K.: Instant Neural Control of a Movement Signal. Nature 416, 141–142 (2002)CrossRefGoogle Scholar
  4. 4.
    Millán, J.R., Ferrez, P.W., Buttfield, A.: Non Invasive Brain-Machine Interfaces - Final Report. IDIAP Research Institute - ESA (2005)Google Scholar
  5. 5.
    Inoue, S., Akiyama, Y., Izumi, Y., Nishijima, S.: The Development of BCI Using Alpha Waves for Controlling the Robot Arm. IEICE Transactions on Communications 91(7), 2125–2132 (2008)CrossRefGoogle Scholar
  6. 6.
    Iáñez, E., Azorín, J.M., Úbeda, A., Ferrández, J.M., Fernández, E.: Mental Tasks-Based Brain–Robot Interface. Robotics and Autonomous Systems 58(12), 1238–1245 (2010)CrossRefGoogle Scholar
  7. 7.
    Obermaier, B., Müller, G.R., Pfurtscheller, G.: Virtual Keyboard Controlled by Spontaneous EEG Activity. IEEE Transactions on Neural System Rehabilitation Engineering 11, 422–426 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    Sirvent, J.L., Azorín, J.M., Iáñez, E., Úbeda, A., Fernández, E.: P300-based Brain-Computer Interface for Internet Browsing. In: IEEE International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS), pp. 615–622 (2010)Google Scholar
  9. 9.
    Bayliss, J.D.: Use of the Evoked Potential P3 Component for Control in a Virtual Environment. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11, 113–116 (2003)CrossRefGoogle Scholar
  10. 10.
    Pfurtscheller, G., Neuper, C.: Motor Imagery and Direct Brain-Computer Communication. Proceedings of the IEEE 89, 1123–1134 (2001)CrossRefGoogle Scholar
  11. 11.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces. Journal of Neural Engineering 4, 1–13 (2007)CrossRefGoogle Scholar
  12. 12.
    Prasad, V.S., Murthy, J.M.K., Sailaja, S.: Surface Mapping of Spike Potential Fields: Visual vs. Quantitative EEG Analysis. Neurology India 50, 181–183 (2002)Google Scholar
  13. 13.
    Sebastián, M.V., Navascués, M.A., Valdizán, J.R.: Surface Laplacian and Fractal Brain Mapping. Journal of Computational and Applied Mathematics 189, 132–141 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Wong, M.T.H., Lieh-Mak, F.: Topographic Brain Mapping of EEG and Evoked Potentials in Chinese Normal and Psychiatric Patients - Preliminary Findings. J.H.K.C. Psych. 1, 6–11 (1991)Google Scholar
  15. 15.
    Kennerly, R.: QEEG analysis of cranial electrotherapy: A pilot study. Journal of Neurotherapy 8(2), 112–113 (2004)Google Scholar
  16. 16.
  17. 17.
    Millán, J.d.R.: On the Need for On-line Learning in Brain-Computer Interfaces. International Joint Conference on Neural Networks (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrés Úbeda
    • 1
  • Eduardo Iáñez
    • 1
  • José M. Azorín
    • 1
  • Eduardo Fernández
    • 1
  1. 1.Biomedical Neuroengineering GroupMiguel Hernández University of ElcheElcheSpain

Personalised recommendations