Improved Classification Methods for Brain Computer Interface System
Brain Computer Interface (BCI) aims at providing a new communication way without brain's normal output through nerve and muscle. The Electroencephalography (EEG) has been widely used for BCI system because it is a non-invasive approach. For the EEG signals of left and right hand motor imagery, the Event-Related Resynchronization (ERD) and Event-Related Synchronization (ERS) are used as classification features in this paper. The raw data are transformed by nonlinear methods and classified by Fisher classifier. Compared with the linear methods, the classification accuracy can get an obvious increase to 86.25%.