Abstract
The greatest problem met when a Brain Computer Interface (BCI) based on electroencephalographic (EEG) signals is to be created is a huge dimensionality of EEG feature space and at the same time very limited number of possible observations. The first is a result of a huge amount of data which can be recorded during the single trial, the latter - the result of individuality of EEG signals, which can significantly differ in different frequency bands determined for different subjects. These two reasons force the brain researches to reduce the huge EEG feature space to only some features, those which allow to build a BCI of a satisfactory accuracy. The paper presents the comparison of two methods of feature selection - blind source separation (BSS) method and method using interpretable features. The comparison was carried out with the data set recorded during EEG session with a subject whose task was to imagine movements of right and left hand.