| ICA+CSSD的脑-机接口分类 |
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| 引用本文： | 唐艳,柳建新,龚安栋. ICA+CSSD的脑-机接口分类[J]. 电子科技大学学报(社会科学版), 2008, 0(3) |
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| 作者姓名： | 唐艳 柳建新 龚安栋 |
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| 作者单位： | 中南大学信息物理学院;中南大学信息物理学院 长沙;长沙410083中南大学信息科学与工程学院长沙; |
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| 基金项目： | 湖南省自然科学基金(07JJ6045) |
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| 摘 要： | 针对复杂的脑电问题,介绍了一种对单次思维脑电信号提取、分类的方法。该方法的主要思想是将独立成分分量和共空域子空间分解方法以及支持向量机学习方法结合起来,用于提取脑电信号特征。该方法分别被用于BCI Competition 2003 Data set IV和BCI Competition III Data set I,正确率分别达到了89%和92%。实验证明独立成分分量算法和共空域子空间分解方法能够很好地结合起来进行脑思维的分类,分类正确率很高,是一种快速、稳定可行的分解方法。
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| 关 键 词： | 脑-机接口 脑电信号 独立分量分析 支持向量机 |
Classification of Brain-Computer Interfaces Using ICA+CSSD |
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| TANG Yan,LIU Jian-xin,, GONG An-dong. Classification of Brain-Computer Interfaces Using ICA+CSSD[J]. Journal of University of Electronic Science and Technology of China(Social Sciences Edition), 2008, 0(3) |
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| Authors: | TANG Yan LIU Jian-xin GONG An-dong |
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| Affiliation: | TANG Yan1,LIU Jian-xin1,, GONG An-dong1,2 (1. Department of Info-Physics Engineering,Central South University Changsha 410083,2. Department of Information Science , Engineering,Central South University Changsha 410083) |
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| Abstract: | Identification and classification technology plays an important part in study of the brain-computer interface (BCI) system. In this paper, an algorithm is presented to deals with the complex brain signals and extract features and classify single-trial electroencephalogram (EEG). The algorithm combines independent component analysis algorithm and common spatial subspace decomposition with support vector machine to extract features from multi-channel EEG and electro cortico gram (ECoG). This algorithm was app... |
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| Keywords: | brain-computer interface electroencephalograms independent component analysis (ICA) support vector machine |
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