Channel Selection and Feature Extraction of ECoG-based Brain-Computer Interface using Band Power
Electrocorticography (ECoG) signals have been proved to be associated with different types of motor imagery and have used in brain-computer interface (BCI) research.This paper studies the channel selection and feature extraction using band powers (BP) for a typical ECoG-based BCI system.The subject images movement of left finger or tongue.Firstly,BP features were used for channel selection,and 11 channels which had distinctive features were selected from 64 channels.Then,the features of ECoG signals were extracted using BP,and the dimension of feature vector was reduced with principal components analysis (PCA).Finally,Fisher linear discriminant analysis (LDA) was used for classification.The results of the experiment showed that this algorithm has got good classification accuracy for the test data set.
brain-computer interface band power channel selection feature extraction linear discriminant analysis
Haibin Zhao Chong Liu Chunyang Yu Hong Wang
School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
国际会议
重庆
英文
3564-3568
2010-12-11（万方平台首次上网日期，不代表论文的发表时间）