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Journal of Medical Systems

, Volume 36, Supplement 1, pp 51–63 | Cite as

Automatic and Adaptive Classification of Electroencephalographic Signals for Brain Computer Interfaces

  • Germán Rodríguez-Bermúdez
  • Pedro J. García-Laencina
Original Paper

Abstract

Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.

Keywords

Biomedical engineering Electroencephalographic signals Brain computer interface Feature selection Linear discrimination Adaptive systems 

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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Germán Rodríguez-Bermúdez
    • 1
  • Pedro J. García-Laencina
    • 1
  1. 1.Centro Universitario de la Defensa de San Javier (University Centre of Defence at the Spanish Air Force Academy)Santiago de la RiberaSpain

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