Feature Selection using Relative Wavelet Energy for Brain-Computer Interface Design

摘要：

The critical issues in brain-computer interface (BCI) research is how to translate a persons intention into brain signals for controlling computer program or wheelchair. In this paper, we used a new method: relative wavelet energy (RWE) for feature selection in BCIs design and linear discriminant analysis LDA) and support vector machine (SVM) were utilized to classify the pattern of left and right hand movement imagery. Its performance was evaluated by mutual information (MI) using the data set IIIb of BCI Competition III. This technology provides another useful way to EEG feature selection in BCIs research.

关键词： EEG brain-computer interface (BCI) wavelet transform relative wavelet energy (RWE)

作者: Zhao Haibin Wang Xu Wang Hong

作者单位: School of Information Science and Engineering Northeastern University Shenyang city, China School of Mechanical Engineering and Automation Northeastern University Shenyang city, China

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 1434-1437

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）