论文标题
用于运动图像分类的SPD歧管上的图形神经网络：时频分析的视角
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
论文作者
论文摘要
运动图像（MI）分类一直是基于脑电图（EEG）的大脑计算机界面中的重要研究主题。在过去的几十年中，MI-EEG分类器的性能逐渐增强。在这项研究中，我们从时频分析的角度放大了基于几何深度学习的MI-EEG分类器，引入了一种称为Graph-CPNET的新体系结构。我们将此类别的分类器称为几何分类器，突出了它们的基础，其基础是由EEG空间协方差矩阵引起的差异几何形状。 Graph-cpnet利用新颖的流动图形卷积技术来捕获时频域中的EEG特征，从而在信号分段中为捕获局部波动提供了提高的灵活性。为了评估Graph-cpSPNET的有效性，我们采用了五个常用的公开可用的MI-EEG数据集，在11个场景中有9个方案中达到了几乎最佳的分类精度。可以在https://github.com/geometricbci/tensor-cspnet-and-graph-cspnet上找到Python存储库。
The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In this study, we amplify the geometric deep learning-based MI-EEG classifiers from the perspective of time-frequency analysis, introducing a new architecture called Graph-CSPNet. We refer to this category of classifiers as Geometric Classifiers, highlighting their foundation in differential geometry stemming from EEG spatial covariance matrices. Graph-CSPNet utilizes novel manifold-valued graph convolutional techniques to capture the EEG features in the time-frequency domain, offering heightened flexibility in signal segmentation for capturing localized fluctuations. To evaluate the effectiveness of Graph-CSPNet, we employ five commonly-used publicly available MI-EEG datasets, achieving near-optimal classification accuracies in nine out of eleven scenarios. The Python repository can be found at https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet.
