Multiplying the Mileage of Your Dataset with Subwindowing

  • Adham Atyabi
  • Sean P. Fitzgibbon
  • David M. W. Powers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6889)

Abstract

This study is focused on improving the classification performance of EEG data through the use of some data restructuring methods. In this study, the impact of having more training instances/samples vs. using shorter window sizes is investigated. The BCI2003 IVa dataset is used to examine the results. The results not surprisingly indicate that, up to a certain point, having higher numbers of training instances significantly improves the classification performance while the use of shorter window sizes tends to worsen performance in a way that usually cannot fully be compensated for by the additional instances, but tends to provide useful gain in overall performance for small divisors into two or three subepochs. We have moreover determined that use of an incomplete set of overlapping windows can have little effect, and is inapplicable for the smallest divisors, but that use of overlapping subepochs from three specific non-overlapping areas (start, middle and end) of a superepoch tends to contribute significant additional information. Examination of a division into five equal non-overlapping areas indicates that for some subjects the first or last fifth contributes significantly less information than the middle three fifths.

Keywords

Electroencephalogram Window size Overlapping window 

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References

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    Fitzgibbon, S.: A Machine Learning Approach to Brain-Computer Interfacing. School of Psychology. Faculty of Social Sciences. Flinders University (2007)Google Scholar
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    Powers, D.M.W.: Recall and Precision versus the Bookmaker. In: International Conference on Cognitive Science (ICSC-2003), Sydney, Australia, pp. 529–534 (2003)Google Scholar
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    Blankertz, B., Muller, K.-R., Krusienski, D.J., Schalk, G., Wolpaw, J.R., Schlogl, A., Pfurtscheller, G., del Millan, J.R., Schroder, M., Birbaumer, N.: The BCI competition III:Validating alternative approaches to actual BCI problems. IEEE Trans. on Neural Syst. Rehabil. Eng. 14(2), 153–159 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adham Atyabi
    • 1
  • Sean P. Fitzgibbon
    • 1
  • David M. W. Powers
    • 1
  1. 1.School of Computer Science, Engineering and Mathematics (CSEM)Flinders UniversityAustralia

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