In brain-computer interface (BCI), feature extraction is the key to the accuracy of recognition. There is important local structural information in the EEG signals, which is effective for classification; and this locality of EEG features not only exists in the spatial channel position but also exists in the frequency domain. In order to retain sufficient spatial structure and frequency information, we use one-versus-rest filter bank common spatial patterns (OVR-FBCSP) to preprocess the data and extract preliminary features. On this basis, we conduct research and discussion on feature extraction methods. One-dimensional feature extraction methods like linear discriminant analysis (LDA) may destroy this kind of structural information. Traditional manifold learning methods or two-dimensional feature extraction methods cannot extract both types of information at the same time. We introduced the bilinear structure and matrix-variate Gaussian model into two-dimensional discriminant locality preserving projection (2DDLPP) algorithm and decompose EEG signals into spatial and spectral parts. Afterwards, the most discriminative features were selected through a weight calculation method. We tested the method on BCI competition data sets 2a, data sets IIIa, and data sets collected by our laboratory, and the results were expressed in terms of recognition accuracy. The cross-validation results were 75.69%, 70.46%, and 54.49%, respectively. The average recognition accuracy of new method is improved by 7.14%, 7.38%, 4.86%, and 3.8% compared to those of LDA, two-dimensional linear discriminant analysis (2DLDA), discriminant locality property projections (DLPP), and 2DDLPP, respectively. Therefore, we consider that the proposed method is effective for EEG classification.

中文翻译：

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使用流形学习和矩阵变量高斯模型的脑电信号分类

在脑机接口（BCI）中，特征提取是识别准确率的关键。脑电信号中存在重要的局部结构信息，对分类有效；而EEG特征的这种局部性不仅存在于空间通道位置，也存在于频域。为了保留足够的空间结构和频率信息，我们使用一对多滤波器组通用空间模式（OVR-FBCSP）对数据进行预处理并提取初步特征。在此基础上，我们对特征提取方法进行了研究和讨论。线性判别分析（LDA）等一维特征提取方法可能会破坏这种结构信息。传统的流形学习方法或二维特征提取方法不能同时提取两种类型的信息。我们将双线性结构和矩阵变量高斯模型引入二维判别局部保持投影（2DDLPP）算法，并将脑电信号分解为空间和光谱部分。之后，通过权重计算方法选择最具辨别力的特征。我们在 BCI 竞赛数据集 2a、数据集 IIIa 和我们实验室收集的数据集上测试了该方法，结果以识别准确率表示。交叉验证结果分别为 75.69%、70.46% 和 54.49%。与LDA相比，新方法的平均识别准确率分别提高了7.14%、7.38%、4.86%和3.8%，二维线性判别分析 (2DLDA)、判别局部属性投影 (DLPP) 和 2DDLPP。因此，我们认为所提出的方法对脑电图分类是有效的。