摘要
脑电信号是一种典型的非平稳随机信号,对脑电信号的分类识别是非常困难的,为了提高正确识别率,提出多导脑电信号的分类识别方法。首先对受试者分别在睁眼和闭眼状态下的单导脑电信号进行特征提取,然后选取多组识别效果不好的单导联的特征,组合成为多导脑电信号特征,最后用RBF核函数的支持向量机分类器进行分类识别。结果表明对多导联特征的正识率比单导联正识率有很大提高。
Nonstationary randomness signal (NRS) is difficult to classify and recognize. In order to improve the performance of the classifying technique of NRS, a novel technique for classifying multi-channel EEG signal is introduced in this paper. First of all, subjects in the states of one eye open and one eye closed with a single-channel EEG feature are extracted, then the characteristics of single-channel EEG signal with bad classifying results are selected and combined into multi-channel EEG characteristics. Finally, RBF Kernel Support Vector Machine classifier is used to classify the characteristics under different states. The results show that the correct classification rate is greatly improved.
出处
《机电工程技术》
2008年第8期73-75,共3页
Mechanical & Electrical Engineering Technology
基金
国家自然科学基金项目(编号:30470459)
河南省教育厅自然基金(编号:2008A130002)
关键词
脑电信号
多导联
支持向量机
正识率
EEG
multi-channel
support vector machine
ratio of correct recognition
