| 基于Hilbert-Huang变换的思维脑电分类技术研究 |
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| 引用本文： | 耿丽硕, 范影乐,.基于Hilbert-Huang变换的思维脑电分类技术研究[J].电子器件,2009,32(2). |
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| 作者姓名： | 耿丽硕 范影乐 |
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| 作者单位： | 杭州电子科技大学,生物医学工程与仪器研究所,杭州,310018 |
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| 基金项目： | 国家自然科学基金，浙江省新苗人才计划项目 |
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| 摘 要： | 研究基于Hilbert-Huang变换的思维脑电分类方法.对思维脑电信号进行Hilbert-Huang时频预处理,经经验模式分解后,得到多阶固有模态分量.然后将经HHT变换后的时频窗口内的振幅标准差作为不同心理作业信号特征,再应用K-近邻对思维脑电信号进行分类决策.通过对Colorado州立大学EEG研究中心的三类思维脑电心理作业样本进行分类,平均正确率达到82.54%.经Hilbert-Huang变换得到的脑电信号特征,可以作为思维脑电分类的有效依据.
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| 关 键 词： | 思维脑电 心理作业分类 Hilbert-Huang变换 振幅标准差 K-近邻 |
Research of Mental EEG Classification Based on Hilbert-Huang Transform |
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| GENG Li-shuo,FAN Ying-le.Research of Mental EEG Classification Based on Hilbert-Huang Transform[J].Journal of Electron Devices,2009,32(2). |
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| Authors: | GENG Li-shuo FAN Ying-le |
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| Affiliation: | Institute of Biomedical Engineering and Instrument;Hangzhou Dianzi Uuiversity;Hangzhou 310018;China |
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| Abstract: | This paper studies the classification of EEG signals during mental tasks based on Hilbert-Huang transform method.The mental EEG signals was preprocessed by Hilbert-Huang transform in the time-frequency field.With empirical mode decomposition(EMD),the data set was decomposed into a finite and often small number of intrinsic mode functions(IMF).Using Hilbert transform to those IMF components yielded instantaneous amplitude and frequency.After obtaining features parameters of different mental tasks by using th... |
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| Keywords: | mental EEG mental tasks' classification Hilbert-Huang transform amplitude standard deviation K-neighbors |
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