Feature Extraction Using Wavelet Entropy and Band Powers in Brain-Computer Interface
Brain-computer interface (BCI) uses brain activity for communication and control of objects in their environment without the participation of peripheral nerves and muscles. BCI technology can help improve the quality of life and restore functions for people with severe motor disabilities. We used combinations of wavelet entropy (WE) and band powers (BP) for feature extraction in BCI system which was based on imaginary left and right hand movements. Linear discriminant analysis (LDA) was used for classification and mutual information (MI) was used for evaluation because it take into account the magnitude of the outputs. This algorithm was applied on the data set III of BCI competition 2003 and got good results. The results of the experiment showed that this algorithm was a very good method for feature extraction in BCI system.
brain-computer interface wavelet transform wavelet entropy bandpowers linear discriminant analysis
Haibin Zhao Chong Liu Chunsheng Li Hong Wang
School of Mechanical Engineering and Automation Northeastern University Shenyang, China
国际会议
2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)
大连
英文
1510-1513
2010-07-05（万方平台首次上网日期，不代表论文的发表时间）