Study on Identification Algorithm of EEG Imaginary Movements
Movement whether it is actual or imaginary can produce different electroencephalogram (EEG) signals.How to extract features of signals and accurately classify them is a key to brain-computer interface(BCI) system.In the paper,BCI competition data downloaded from BCI website are used as study object,through time-domain analysis and frequency-domain analysis,according to the attribute of event-related synchronization (ERS) and event-related desynchronization (ERD) during imagery movement,energy difference of lead C3 and C4 are selected as features and wavelet package is used to extract them.Probabilistic neural networks (PNN) is used as classification method.Compared with other two calssification methods such as support vector method (SVM) and liner classifier,the classification accuracy rate of PNN reaches to 89.2% steadily and is higher than them.It is proved that the method provided in the paper are effective for identifying imaginary movements.
Brain-computer interface (BCI) event-related desynchronization (ERD / event-related synchronization (ERS) AR wavelet package analysis PNN
Jing Zhou Lijun Li Ningshan Li Xiaoming Wu Rongqian Yang
The Department of Biomedical Engineering,South China University of Technology,Guangzhou,510006,China
国际会议
沈阳
英文
1885-1889
2012-09-07（万方平台首次上网日期，不代表论文的发表时间）