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....

A new algorithm for Electroencephalogram (EEG) signals classification is proposed in this paper. This classification is used for automatic detection of patients with epilepsy in a medical system for decision support. The classification algorithm is based on Ordered Fuzzy Decision Tree (OFDT) for EEG signals. The application of OFDT requires special transformation of EEG signal that is named as ...

The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically si...