Classification of EEG signals using feature creation produced by grammatical evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Tzallas:2016:TELFOR,

• 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

Citations