| 基于PCA和LDA数据降维的脑磁图脑机接口研究 |
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| 作者姓名： | Wang J Zhou L |
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| 作者单位： | 燕山大学信息科学与工程学院; |
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| 基金项目： | 国家自然科学基金资助项目(60504035,61074195); 河北省自然科学基金资助项目(F2010001281,A2010001124) |
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| 摘 要： | 脑磁图(MEG)信号作为一种新的脑-机接口(BCI)输入信号,含有手运动方向的模式信息。通常对MEG信号采用信号处理的特征提取和线性分类,识别率一直难于提高。本文提出用主成分分析(PCA)方法对其进行特征提取,并用线性判别分析(LDA)进行了优化,最后用最近邻非线性分类器进行分类,在分类结果的基础上分析了混淆矩阵。实验结果表明PCA+LDA方法能有效的分析多通道的MEG信号,平均识别率达到了53.0%,优于BCI竞赛Ⅳ的识别率46.9%。
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| 关 键 词： | 脑-机接口 脑磁图 主成分分析 线性判别分析 混淆矩阵 |
Research on magnetoencephalography-brain computer interface based on the PCA and LDA data reduction |
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| Wang J,Zhou L.Research on magnetoencephalography-brain computer interface based on the PCA and LDA data reduction[J].Journal of Biomedical Engineering,2011,28(6):1069-1074. |
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| Authors: | Wang Jinjia Zhou Lina |
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| Institution: | Wang Jinjia Zhou Lina (College of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China) |
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| Abstract: | The magnetoencephalography(MEG) can be used as a control signal for brain computer interface(BCI).The BCI also includes the pattern information of the direction of hand movement.In the MEG signal classification,the feature extraction based on signal processing and linear classification is usually used.But the recognition rate has been difficult to improve.In the present paper,a principal component analysis(PCA) and linear discriminant analysis(LDA) method has been proposed for the feature extraction,and the... |
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| Keywords: | Brain computer interface(BCI) Magnetoencephalography(MEG) Principal component analysis(PCA) Linear discriminant analysis(LDA) Confusion matrix |
| 本文献已被 CNKI PubMed 等数据库收录！ |
