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Feature extraction for on-line EEG classification using principal components and linear discriminants

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Abstract

The study focuses on the problems of dimensionality reduction by means of principal component analysis (PCA) in the context of single-trial EEG data classification (i.e. discriminating between imagined left- and right-hand movement). The principal components with the highest variance, however, do not necessarily carry the greatest information to enable a discrimination between classes. An EEG data set is presented where principal components with high variance cannot be used for discrimination. In addition, a method based on linear discriminant analysis (LDA), is introduced that detects principal components which can be used for discrimination, leading to data sets of reduced dimensionality but similar classification accuracy.

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Authors and Affiliations

  1. Ludwig Boltzmann-Institute for Medical Informatics & Neuroinformatics, Graz, Austria

    K. Lugger, D. Flotzinger, A. Schlögl &  G. Pfurtscheller

  2. Institute for Biomedical Engineering, Department of Medical Informatics, University of Technology, Brockmanngasse 41, A-8010, Graz, Austria

    M. Pregenzer &  G. Pfurtscheller

Authors
  1. K. Lugger
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  2. D. Flotzinger
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  3. A. Schlögl
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  4. M. Pregenzer
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  5. G. Pfurtscheller
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Corresponding author

Correspondence to G. Pfurtscheller.

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Lugger, K., Flotzinger, D., Schlögl, A. et al. Feature extraction for on-line EEG classification using principal components and linear discriminants. Med. Biol. Eng. Comput. 36, 309–314 (1998). https://doi.org/10.1007/BF02522476

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  • DOI: https://doi.org/10.1007/BF02522476

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