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Study of discriminant analysis applied to motor imagery bipolar data

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Abstract

We present a study of linear, quadratic and regularized discriminant analysis (RDA) applied to motor imagery data of three subjects. The aim of the work was to find out which classifier can separate better these two-class motor imagery data: linear, quadratic or some function in between the linear and quadratic solutions. Discriminant analysis methods were tested with two different feature extraction techniques, adaptive autoregressive parameters and logarithmic band power estimates, which are commonly used in brain–computer interface research. Differences in classification accuracy of the classifiers were found when using different amounts of data; if a small amount was available, the best classifier was linear discriminant analysis (LDA) and if enough data were available all three classifiers performed very similar. This suggests that the effort needed to find regularizing parameters for RDA can be avoided by using LDA.

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Acknowledgments

We would like to thank our reviewers for their interest and useful comments. This work was supported by the Spanish Ministry of Culture and Education (Grant Ref.: AP-2000-4673), by the Lorenz Böhler Gesellschaft in Austria and by “Fonds zur Förderung der wissenschaftlichen Forschung” in Austria, project 16326-BO2.

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Authors and Affiliations

  1. Dp IEE, Edificio Los Tejos, Public University of Navarre, Campus Arrosadia s/n, Pamplona, 31006, Spain

    Carmen Vidaurre & Rafael Cabeza

  2. Institute for Knowledge Discovery, TUG, Krenngasse 37, Graz, 8010, Austria

    Reinhold Scherer & Gert Pfurtscheller

  3. Institute for Human–Computer Interfaces, TUG, Krenngasse 37, Graz, 8010, Austria

    Alois Schlögl

Authors
  1. Carmen Vidaurre
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  2. Reinhold Scherer
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  3. Rafael Cabeza
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  4. Alois Schlögl
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  5. Gert Pfurtscheller
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Correspondence to Carmen Vidaurre.

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Vidaurre, C., Scherer, R., Cabeza, R. et al. Study of discriminant analysis applied to motor imagery bipolar data. Med Bio Eng Comput 45, 61–68 (2007). https://doi.org/10.1007/s11517-006-0122-5

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  • DOI: https://doi.org/10.1007/s11517-006-0122-5

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