摘要
脑机接口(BCI)系统通过从脑信号中提取特征对其进行识别。针对自回归模型特征提取方法和传统主成分分析降维方法处理多通道信号的局限性,本文提出了多变量自回归(MVAR)模型和多线性主成分分析(MPCA)结合的多通道特征提取方法,并用于脑磁图/脑电图(MEG/EEG)信号识别。首先计算MEG/EEG信号的MVAR模型的系数矩阵,然后采用MPCA对系数矩阵进行降维,最后使用线性判别分析分类器对脑信号分类。创新在于将传统单通道特征提取方法扩展到多通道。选用BCI竞赛IV数据集3和1数据进行实验验证,两组实验结果表明MVAR和MPCA结合的特征提取方法处理多通道信号是可行的。
Brain-computer interface（BCI）systems identify brain signals through extracting features from them.In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals,this paper presents a multichannel feature extraction method that multivariate autoregressive（MVAR）model combined with the multiple-linear principal component analysis（MPCA）,and used for magnetoencephalography（MEG）signals and electroencephalograph（EEG）signals recognition.Firstly,we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method,and then reduced the dimensions to a lower one,using MPCA.Finally,we recognized brain signals by Bayes Classifier.The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one.We then carried out the experiments using the data groups ofⅣ_Ⅲ andⅣ_Ⅰ.The experimental results proved that the method proposed in this paper was feasible.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2015年第1期19-24,共6页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(61473339)
中国博士后科学基金资助项目(2014M561202)
河北省2014年度博士后专项资助项目(B2014010005)
首批"河北省青年拔尖人才"资助项目
关键词
脑机接口
脑磁图
脑电图
多变量自回归模型
多线性主成分分析
特征提取
brain-computer interface
magnetoencephalography
electroencephalograph
multivariate autoregressive model
multi-linear principal component analysis
feature extraction
