Advertisement

EEG Dataset Reduction and Classification Using Wave Atom Transform

  • Ignas Martisius
  • Darius Birvinskas
  • Robertas Damasevicius
  • Vacius Jusas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)

Abstract

Brain Computer Interface (BCI) systems perform intensive processing of the electroencephalogram (EEG) data in order to form control signals for external electronic devices or virtual objects. The main task of a BCI system is to correctly detect and classify mental states in the EEG data. The efficiency (accuracy and speed) of a BCI system depends upon the feature dimensionality of the EEG signal and the number of mental states required for control. Feature reduction can help improve system learning speed and, in some cases, classification accuracy. Here we consider Wave Atom Transform (WAT) of the EEG data as a feature reduction method. WAT takes input data and concentrates its energy in a few transform coefficients. WAT is used as a data preprocessing step for feature extraction. We use artificial neural networks (ANNs) for classification and perform research with varying number of neurons in a hidden layer and different network training functions (Levenberg-Marquardt, Conjugate Gradient Backpropagation, Bayesian Regularization). The novelty of the paper is the application of WAT in the EEG data processing. We conclude that the method can be successfully used for feature extraction and dataset feature reduction in the BCI domain.

Keywords

EEG Brain Computer Interface Wave Atom Transform dimensionality reduction classification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Subasi, A., Ercelebi, E.: Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine 78(2), 87–99 (2005)CrossRefGoogle Scholar
  2. 2.
    Martisius, I., Sidlauskas, K., Damasevicius, R.: Real-Time Training of Voted Perceptron for Classification of EEG Data. International Journal of Artificial Intelligence 10(S13), 41–50 (2013)Google Scholar
  3. 3.
    Birvinskas, D., Jusas, V., Martisius, I., Damasevicius, R.: EEG dataset reduction and feature extraction using discrete cosine transform. In: Proc. of UKSim-AMSS EMS 2012: 6th European Modelling Symposium on Mathematical Modeling and Computer Simulation, Malta, November 14-16, pp. 186–191 (2012)Google Scholar
  4. 4.
    Addison, P.A.: Wavelet transforms and the ECG: a review. Physiological Measurement 26, 155–199 (2005)CrossRefGoogle Scholar
  5. 5.
    Demanet, L., Ying, L.: Wave atoms and sparsity of oscillatory patterns. Appl. Comput. Harmon. Anal. 23(3), 368–387 (2007)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Mohammed, A.A., Jonathan Wu, Q.M., Sid-Ahmed, M.A.: Application of Wave Atoms Decomposition and Extreme Learning Machine for Fingerprint Classification. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 246–255. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Herrmann, F.J., Friedlander, M.P., Yilmaz, O.: Fighting the Curse of Dimensionality: Compressive Sensing in Exploration Seismology. IEEE Signal Processing Magazine 29(3), 88–100 (2012)CrossRefGoogle Scholar
  8. 8.
    Aggarwal, V., Patterh, M.S.: ECG Compression using Wavelet Packet, Cosine Packet and Wave Atom Transforms. Int. Journal of Electronic Engineering Research 1(3), 259–268 (2009)Google Scholar
  9. 9.
    Rajeesh, J.: Rician noise removal on MRI using wave atom transform with histogram based noise variance estimation. In: IEEE Int. Conf. on Communication Control and Computing Technologies (ICCCCT), October 7-9, pp. 531–535 (2010)Google Scholar
  10. 10.
    Geetika, D., Varun, R.: MRI Denoising Using Waveatom Shrinkage. Global Journal of Researches in Engineering 12(4) (2012)Google Scholar
  11. 11.
    Martišius, I., Damaševičius, R.: Class-adaptive denoising for EEG data classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 302–309. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Birbaumer, N., Flor, H., Ghanayim, N., Hinterberger, T., Iverson, I., Taub, E., Kotchoubey, B., Kbler, A., Perelmouter, J.: A Brain-Controlled Spelling Device for the Completely Paralyzed. Nature 398, 297–298Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ignas Martisius
    • 1
  • Darius Birvinskas
    • 1
  • Robertas Damasevicius
    • 1
  • Vacius Jusas
    • 1
  1. 1.Software Engineering DepartmentKaunas University of TechnologyKaunasLithuania

Personalised recommendations