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A Theoretical Derivation of the Kernel Extreme Energy Ratio Method for EEG Feature Extraction

  • Shiliang Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)

Abstract

In the application of brain-computer interfaces (BCIs), energy features are both physiologically well-founded and empirically effective to describe electroencephalogram (EEG) signals for classifying brain activities. Recently, a linear method named extreme energy ratio (EER) for energy feature extraction of EEG signals in terms of spatial filtering was proposed. This paper gives a nonlinear extension of the linear EER method. Specifically, we use the kernel trick to derive a kernelized version of the original EER feature extractor. The solutions for optimizing the criterion in kernel EER are provided for future use.

Keywords

brain-computer interface (BCI) extreme energy ratio (EER) EEG signal classification feature extraction kernel machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shiliang Sun
    • 1
  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

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