Testing Extreme Learning Machine in Motor Imagery Brain Computer Interface

EEG signal is considered a dynamical system, difficult and complex to learn. Therefore Brain Computer Interface Systems need to manage specific time variations of the EEG since the extracted feature are non-stationary. This paper presents a study to test Extreme Learning Machine as a suitable classification method for Motor Imagery Brain Computer Interface. In order to take in to account the time course of the signals new descriptors from three widely known Feature Extraction methods (Power Spectral Density, Hjorth parameters and Adaptive AutoregRessive coefficients) have been obtained by three different techniques: central window, averaging features and linking features. Results shows that these new descriptors have improved the performance of the Extreme Learning Machine with respect classical techniques.