High Performance Classification of Two Imagery Tasks in the Cue-Based Brain Computer Interface

  • Omid Dehzangi
  • Mansoor Zolghadri Jahromi
  • Shahram Taheri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4774)


Translation of human intentions into control signals for a computer, so called Brain-Computer Interface (BCI), has been a growing research field during the last years. In this way, classification of mental tasks is under investigation in the BCI society as a basic research. In this paper, a Weighted Distance Nearest Neighbor (WDNN) classifier is presented to improve the classification rate between the left and right imagery tasks in which a weight is assigned to each stored instance. The specified weight of each instance is then used for calculating the distance of a test pattern to that instance. We propose an iterative learning algorithm to specify the weights of training instances such that the error rate of the classifier on training data is minimized. ElectroEncephaloGram (EEG) signals are caught from four familiar subjects with the cue-based BCI. The proposed WDNN classifier is applied to the band power and fractal dimension features, which are extracted from EEG signals to classify mental tasks. Results show that our proposed method performs better in some subjects in comparison with the LDA and SVM, as well-known classifiers in the BCI field.


Nearest Neighbor Weighted distance Brain-Computer Interface EEG 


  1. 1.
    Pfurtscheller, G., Neuper, C.: Motor imagery and direct brain-computer communication. In: Proc. IEEE, pp. 1123–1134. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  2. 2.
    Pfurtscheller, G., Lopes, S.: Event related desynchronization: Hand book of electroenceph. And clininical Neurophisiology. Revised edition, vol. 6. Elsevier, Amsterdam (1999)Google Scholar
  3. 3.
    Bozorgzadeh, Z., Birch, G.E., Mason, S.G.: The LF-ASD brain computer interface: on-line identification of imagined finger flexions in the spontaneous EEG of able-bodied subjects. In: IEEE Intern. Conf. Acous. Speech proc., vol. 6, pp. 2385–2388 (2000)Google Scholar
  4. 4.
    Boostani, R., Moradi, M.H.: A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier. Journ. Neural Eng. 1, 4 (2004)Google Scholar
  5. 5.
    Boostani, R., Graimann, B., Moradi, M.H., Pfurtscheller, G.: A Comparison Approach toward Finding the Best Feature and Classifier in Cue-Based BCI. Journ. Medic. Bio. Eng. Comp. 6 (2007)Google Scholar
  6. 6.
    Schlögl, A., Flotzinger, D., Pfurtscheller, G.: Adaptive Autoregressive Modeling used for Single-trial EEG Classification. Biomediz. Tech. 42, 162–167 (1997)CrossRefGoogle Scholar
  7. 7.
    Graimann, B., Huggins, J.E., Levine, S.P., Pfurtscheller, G.: Toward a direct brain interface based on human subdural recordings and wavelet-packet analysis. IEEE Trans. Biomed. Eng. 51, 954–962 (2004)CrossRefGoogle Scholar
  8. 8.
    Pfurtscheller, G., Neuper, C., Schlogl, A., Lugger, K.: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters. IEEE Trans. Rehab. Eng. 6, 316–328 (1998)CrossRefGoogle Scholar
  9. 9.
    Haselsteiner, E., Pfurtscheller, G.: Using time-dependent neural networks for EEG classification. IEEE Trans. on Rehab. Eng. 8, 457–463 (2000)CrossRefGoogle Scholar
  10. 10.
    Kalcher, J., Flotzinger, D., Pfurtscheller, G.: A New Approach to a Brain-Computer-Interface (BCI) based on Learning Vector Quantization (LVQ3). In: Proceed. Ann. Intern. Conf. IEEE, vol. 4, pp. 1658–1659 (1992)Google Scholar
  11. 11.
    Flotzinger, D., Pregenzer, M., Pfurtscheller, G.: Feature selection with distinction sensitive learning vector quantisation and genetic algorithms. IEEE Intern. Conf. Comp. Intell. 6, 3448–3451 (1994)Google Scholar
  12. 12.
    Deriche, M., Al-Ani, A.: A new algorithm for EEG feature selection using mutual information. In: IEEE Intern. Conf. Acous. Speech, Signal Proc. ICASSP, vol. 2, pp. 1057–1060 (2001)Google Scholar
  13. 13.
    Cover, T.M., Hart, P.E.: Nearest Neighbor Pattern Classification. IEEE Trans. on Info. Theo. 13, 21–27 (1967)zbMATHCrossRefGoogle Scholar
  14. 14.
    Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Ala. CA (1991)Google Scholar
  15. 15.
    Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood component analysis. Neur. Info. Proc. Sys (NIPS) 17, 513–520 (2004)Google Scholar
  16. 16.
    Weinberger, K., Blitzer, J., Saul, L.: Distance metric learning for large margin nearest neighbor classification. In: Weiss, B.S., Platt, J. (eds.) Advances in Neural Information Processing Systems, p. 18 (2005)Google Scholar
  17. 17.
    Wang, J., Neskovic, P., Cooper, L.N.: Neighbourhood selection in the k-nearest neighbor rule using statistical confidence. Patt. Recog. 39, 417–423 (2006)zbMATHCrossRefGoogle Scholar
  18. 18.
    Wang, J., Neskovic, P., Cooper, L.N.: Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters 28, 207–213 (2007)CrossRefGoogle Scholar
  19. 19.
    Xiao-Yuan, J., David, Z., Yuan-Yan, T.: An improved LDA Approach. IEEE Trans. Sys. Man Cyber. 34, 5 (2004)CrossRefGoogle Scholar
  20. 20.
    Esteller, R.: Detection of Seizure Onset in Epileptic Patients from Intracranial EEG Signals. Ph. D. thesis, School of Electrical and Computer Engineering Georgia Institute of Technology (2000)Google Scholar
  21. 21.
    Higuchi, T.: Approach to an Irregular Time Series on the Basis of Fractal Theory. Physica D 31, 277–283 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Fawcett, T.: ROC Graphs: Notes and Practical Considerations for Researchers, Technical Report HPL-2003-4, HP Labs (2003)Google Scholar
  23. 23.
    Lachiche, N., Flach, P.: Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In: 20th Intern. Conf. Machine Learning (ICML 2003), pp. 416–423 (2003)Google Scholar
  24. 24.
    Vapnic, V.N.: Statistical learning theory. John Wiley and Sons, New York (1998)Google Scholar
  25. 25.
    Fukunaga, K.: Introduction to Statistical Pattern Classification. Academic Press, San Diego, Calif. (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Omid Dehzangi
    • 1
  • Mansoor Zolghadri Jahromi
    • 1
  • Shahram Taheri
    • 1
  1. 1.School of Computer Engineering, Nanyang Technological University, Nanyang AvenueSingapore

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