Classification of EEG signals using feature creation produced by grammatical evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4534

  author =       "Alexandros T. Tzallas and Ioannis Tsoulos and 
                 Markos G. Tsipouras and Nikolaos Giannakeas and 
                 Iosif Androulidakis and Elena Zaitseva",
  title =        "Classification of {EEG} signals using feature creation
                 produced by grammatical evolution",
  booktitle =    "2016 24th Telecommunications Forum (TELFOR)",
  year =         "2016",
  address =      "Belgrade, Serbia",
  month =        "22-23 " # nov,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Feature Extraction, Feature Construction,
                 Classification, EEG, Epilepsy",
  DOI =          "doi:10.1109/TELFOR.2016.7818809",
  size =         "4 pages",
  abstract =     "A state-of-the-art method based on a grammatical
                 evolution approach is used in this study to classify
                 EEG signals. The method is able to construct nonlinear
                 mappings of the original features in order to improve
                 their effectiveness when used as input into artificial
                 intelligence techniques. Several features are initially
                 extracted from the EEG signals which are subsequently
                 used to create the non-linear mappings. Then, a
                 classification stage is applied, using multi-layer
                 perceptron (MLP) and radial basis functions (RBF), to
                 categorize the EEG signals. The proposed method is
                 evaluated using a benchmark epileptic EEG dataset and
                 promising results are reported.",
  notes =        "Also known as \cite{7818809}",

Genetic Programming entries for Alexandros T Tzallas Ioannis G Tsoulos Markos G Tsipouras Nikolaos Giannakeas Iosif Androulidakis Elena Zaitseva