Improved Classification Methods for BCI Based on Nonlinear Transform

Brain computer interface (BCI) aims at providing a new communication way without depending on brain´s normal output through nerve and muscle. The electroencephalography (EEG) has been widely used for BCI because it is a non-invasive approach. For the EEG signals of left and right hand motor imagery, the event-related desynchronization(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%. Two different nonlinear transform were arised and one of them is under the consideration of the relativity of two channels of EEG signals. With these nonlinear transform, different misclassifications can get balance.