论文信息 - Improved Classification Methods for Brain Computer Interface System

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 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, the performance are also stable with the balance of two misclassifications.

Yi Fang | Li Hao | Jin Xiaojie

[1] Zheng Chongxun. Feature Selection and Optimization of Movement-Related EEG Based on Fisher Criterion Time Frequency Analysis , 2008 .

[2] S. Makeig,et al. EEG changes accompanying learned regulation of 12-Hz EEG activity , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3] Wan Xing-yu. Analysis of Right and Left Motor Imagery Based on Empirical Mode Decomposition , 2009 .

[4] Liqing Zhang,et al. EEG-based asynchronous BCI control of a car in 3D virtual reality environments , 2009 .

[5] Steven Lemm,et al. BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.

[6] M.B. Khalid,et al. A Brain Computer Interface (BCI) using Fractional Fourier Transform with Time Domain normalization and heuristic weight adjustment , 2008, 2008 9th International Conference on Signal Processing.

[7] Brendan Z. Allison,et al. Brain-Computer Interfaces , 2010 .

[8] Li Xin,et al. A Virtual Chinese Keyboard BCI System Based on P300 Potentials , 2009 .

[9] Paul Sajda,et al. Brain-Computer Interfaces [from the guest editors] , 2008 .

[10] Stephen J. Roberts,et al. Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation , 2004, IEEE Transactions on Biomedical Engineering.