Modelling and Recognition of Movement Related EEG Signal

Our previous study was aimed at the classification of right index finger movement direction by means of the movement-related EEG signal. The EEG database we used was originally recorded for a physiological research; from our point of view it has one significant drawback: there is no continuous non-movement related (resting) EEG of sufficient length (>10 sec) in the database. To overcome this limitation we decided to generate artificial resting EEG signal. This article describes method and process of non-movement related (resting) EEG signal generation along with the reached new classification results including the false alarm rate. Artificial EEG signal was generated by AR modelling. The AR model parameters were estimated from short segments (3 sec) of resting EEG already present in the database using the autocorrelation method. Owing to large intra-and inter-personal variability one set of parameters had to be estimated for each person and electrode. The artificial EEG was used for the classification with satisfactory classification results.