Method for EEG Feature Extraction Based on Morphological Pattern Spectrum

机译：基于形态模式谱的脑电特征提取方法

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In order to classify the mental tasks in brain-computer interfaces(BCI), a feature extraction method based on morphological pattern spectrum is here proposed. Flat morphological structure element is selected according to the characteristics of electroencephalography(EEG) and morphological features of different scales are obtained with pattern spectrum. Then, support vector machines(SVM) is used as the classifier. Testing results show that the average classification accuracy is up to 97.7% for two kinds of mental tasks and 93.0% for five kinds of mental tasks. This method has a simple calculation and effective feature extraction performance, so it could be a valid method for real time control of EEG.

机译：为了对脑机接口中的心理任务进行分类，提出一种基于形态学模式谱的特征提取方法。根据脑电图（EEG）的特征选择平坦的形态结构要素，并利用图谱获得不同尺度的形态特征。然后，使用支持向量机（SVM）作为分类器。测试结果表明，两种心理任务的平均分类准确率高达97.7％，五种心理任务的平均分类准确率高达93.0％。该方法计算简单，具有有效的特征提取性能，是一种有效的脑电图实时控制方法。

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《Signal Acquisition and Processing, 2009. ICSAP 2009》|2009年|68-72|共5页
会议地点 Kuala Lumpur(MY);Kuala Lumpur(MY)
作者
Han L.J.; Zhang L.J.; Yang J.H.; Li M.; Xu J.W.;
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Mech. Eng. Sch., Univ. of Sci. Technol. Beijing, Beijing, China;

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关键词
brain-computer interfaces; electroencephalography; feature extraction; medical signal processing; signal classification; spectral analysis; support vector machines; BCI system; EEG classification; EEG feature extraction; SVM classifier; mental tasks; morphological pattern spectrum; mental task; pattern spectrum;

机译：脑机接口;脑电图;特征提取;医学信号处理;信号分类;光谱分析;支持向量机; BCI系统; EEG分类; EEG特征提取; SVM分类器;心理任务;形态模式谱;心理任务;模式谱;

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Method for EEG Feature Extraction Based on Morphological Pattern Spectrum

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