Slow Feature Analysis - A Tool for Extraction of Discriminating Event-Related Potentials in Brain-Computer Interfaces

  • Sven Dähne
  • Johannes Höhne
  • Martijn Schreuder
  • Michael Tangermann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6791)

Abstract

The unsupervised signal decomposition method Slow Feature Analysis (SFA) is applied as a preprocessing tool in the context of EEG based Brain-Computer Interfaces (BCI). Classification results based on a SFA decomposition are compared to classification results obtained on Principal Component Analysis (PCA) decomposition and to those obtained on raw EEG channels. Both PCA and SFA improve classification to a large extend compared to using no signal decomposition and require between one third and half of the maximal number of components to do so. The two methods extract different information from the raw data and therefore lead to different classification results. Choosing between PCA and SFA based on classification of calibration data leads to a larger improvement in classification performance compared to using one of the two methods alone. Results are based on a large data set (n=31 subjects) of two studies using auditory Event Related Potentials for spelling applications.

Keywords

Slow Feature Analysis SFA Dimensionality Reduction EEG Brain-Computer Interface BCI Principal Component Analysis PCA Event-Related Potentials ERP Auditory Evoked Potentials AEP 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sven Dähne
    • 1
  • Johannes Höhne
    • 1
  • Martijn Schreuder
    • 1
  • Michael Tangermann
    • 1
  1. 1.Machine-Learning DepartmentBerlin Institute of TechnologyBerlinGermany

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