FEATURE EXTRACTION AND CLASSIFICATION OF TWO-CLASS MOTOR IMAGERY BASED BRAIN COMPUTER INTERFACES

Brain-computer interface (BCI) technology provides a means of communication for people with severe movement disability to communicate with the external world using the electroencephalogram (EEG). In this study, we propose a novel method for extracting the power information contained in specific frequency bands in the context of EEG based BCI. In a two-class Motor Imagery (MI) based BCI, the main objective is to filter EEG signals before feature extraction and classification to achieve higher classification accuracy. First the EEG signal is band-pass filtered in the range of 7Hz and 25 Hz and then the mu and beta rhythms are extracted as features. LDA is then applied as the classifier for classification of left and right motor imagery. It should be noted that the proposed method improves BCI performance when compared to the raw EEG signal