EEG Signal Classification Using Neural Networks

  • George M. Papadourakis
  • Sifis Micheloyannis
  • George Bebis
  • Manolis Giachnakis
Part of the Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 9)


The application of Artificial Neural Networks (ANN) to electroencephalographic (EEG) signal classification is presented. Initially, the power spectrum and coherence “reactivity” parameters are extracted from the EEG signals in order to provide the inputs to the ANNs. In addition, traditional statistical and classification methods are utilized to improve the accuracy of the ANN classifiers. Various ANN experiments are performed and their results are discussed.


Power Spectrum Artificial Neural Network Classifier Parallel Distribute Process Total Power Spectrum ANNs Ability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1991

Authors and Affiliations

  • George M. Papadourakis
    • 1
  • Sifis Micheloyannis
    • 2
  • George Bebis
    • 1
  • Manolis Giachnakis
    • 2
  1. 1.Institute of Computer ScienceFoundation of Research and TechnologyIraklion, CreteGreece
  2. 2.Department of MedicineUniversity of CreteIraklion, CreteGreece

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