A new method of EEG classification for BCI with feature extraction based on higher order statistics of wavelet components and selection with genetic algorithms

  • Authors:

  • Marcin Kołodziej;Andrzej Majkowski;Remigiusz J. Rak

  • Affiliations:

  • Warsaw University of Technology, Warsaw;Warsaw University of Technology, Warsaw;Warsaw University of Technology, Warsaw

  • Venue:
  • ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
  • Year:
  • 2011

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Abstract

A new method of feature extraction and selection of EEG signal for brain-computer interface design is presented. The proposed feature selection method is based on higher order statistics (HOS) calculated for the details of discrete wavelets transform (DWT) of EEG signal. Then a genetic algorithm is used for feature selection. During the experiment classification is conducted on a single trial of EEG signals. The proposed novel method of feature extraction using HOS and DWT gives more accurate results then the algorithm based on discrete Fourier transform (DFT).