Title :
Improved Classification Methods for BCI Based on Nonlinear Transform
Author :
Yi Fang ; Li Hao ; Jin Xiaojie
Author_Institution :
Sch. of Telecommun. Eng., Xidian Univ., Xi´an, China
Abstract :
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.
Keywords :
brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; signal classification; synchronisation; EEG; Fisher classifier; brain computer interface; electroencephalography; event related desynchronization; event related synchronization; motor imagery; nonlinear transform; signal processing; Accuracy; Band pass filters; Electroencephalography; Real time systems; Support vector machines; Training; Transforms;
Conference_Titel :
Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7939-9
Electronic_ISBN :
2156-7379
DOI :
10.1109/ICIECS.2010.5677748