论文标题
使用判别图傅立叶子空间增强基于运动图像的EEG分类
Enhanced motor imagery-based EEG classification using a discriminative graph Fourier subspace
论文作者
论文摘要
处理不规则的域，图形信号处理（GSP）引起了很多关注，尤其是在大脑成像分析中。运动成像任务被广泛用于大脑计算机界面（BCI）系统，该系统使用从脑电图信号中提取的功能执行分类。在本文中，提出了一种基于GSP的方法，用于两级运动图像任务分类。所提出的方法利用了两个矩阵的对角线化，这些矩阵量化了来自每个类别数据的图形光谱表示的协方差结构，从而提供了一个歧视性子空间，其中从数据中提取了独特的特征。在BCI竞争III的数据集IVA上评估了所提出的方法的性能。实验结果表明，所提出的方法的表现优于两种最先进的替代方法。
Dealing with irregular domains, graph signal processing (GSP) has attracted much attention especially in brain imaging analysis. Motor imagery tasks are extensively utilized in brain-computer interface (BCI) systems that perform classification using features extracted from Electroencephalogram signals. In this paper, a GSP-based approach is presented for two-class motor imagery tasks classification. The proposed method exploits simultaneous diagonalization of two matrices that quantify the covariance structure of graph spectral representation of data from each class, providing a discriminative subspace where distinctive features are extracted from the data. The performance of the proposed method was evaluated on Dataset IVa from BCI Competition III. Experimental results show that the proposed method outperforms two state-of-the-art alternative methods.
