Classification of Motor Imagery EEG Signals Based on STFTs

Human motor imagery tasks evoke electroencephalogram (EEG) signal changes. We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings. The technique is based on a time-frequency analysis of EEG signals, regarding the relations between the EEG data obtained from the C3/C4 electrodes, the features were reduced according the Fisher distance. This reduced feature set is finally fed to a linear discriminant for classification. The algorithm was applied to 3 subjects, the classification performance of the proposed algorithm varied between 70% and 93.1%; across subjects.