Classification of EEG signals using feature creation produced by grammatical evolution

A state-of-the-art method based on a grammatical evolution approach is utilized 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.