A feature filtering method for eeg data classification

In this paper, a feature filtering algorithm for brain-computer interface which includes classification of EEG data is proposed. By this method, the features are evaluated according to a criterion based on the Mahalanobis distance between the classes. For some EEG data classification problems, the problem may be determining the features to be extracted, however for the problem of distinguishing between right, left and forward movement imagination, the features that most benefits in classification cannot be determined beforehand. Therefore, features are selected method from a set of all possible features by the proposed filtering to increase the performance and speed of the classifier.