中文摘要：

介绍了非负矩阵分解算法（NMF）的基本原理，给出一种利用NMF进行脑电能量谱特征提取的方法。设计试验对10个被试在三种不同注意任务中的脑电信号进行特征提取，并采用人工神经网络作为分类器进行分类测试。结果表明，NMF算法在高维特征空间具有较强的特征选择能力，其分类正确率明显高于主分量分析（PCA）方法和直接法，三种意识任务的分类正确率分别达到84．5％、88％和86．5％。

英文摘要：

The fundamental of non-negative matrix factorization algorithm was introduced. It is used to extract EEG power spectrum feature. Artificial neural network is employed as classifier. Three level attention mental tasks are designed to test the method. Ten subjects attended the experiment. The classification accuracies indicate that the NMF technique is a powerful feature extractor in high-dimensional feature space. The average classification accuracy of ten subjects achieves 88%, it is higher obviously than that of principal component analysis and direct method.