Feature Extraction of EEG based on Data Reduction

摘要：

An important factor affecting the rate of BCI is the number of EEG features. To reduce the number of features is an important way to improve the speed. In this paper, a method of data reduction be described, features marked be used to discrete the continuous EEG, and then choose the features from the discrete data with the help of this method. The results show that classification accuracy has not been reduced but the number of features is reduction.

关键词： Brain computer interface (BCI) data reduction feature extraction

作者: ZhendongMu PingWang

作者单位: Institute of Information and Technology Jiangxi BlueSky University Nanchang,Jiangxi Province, China

会议类型: 国际会议

会议名称: 2010 International Conference on Computer and Communication Technologies in Agriculture Engineering(计算机与通信技术在农业工程国际会议 CCTAE 2010)

会议地点: 成都

会议语种:英文

页码: 275-277

在线出版日期: 2010-06-12（万方平台首次上网日期，不代表论文的发表时间）