The classification of motor imagery (MI) is a highly sought-after research topic in the field of Electroencephalography (EEG)-based brain-computer interfaces (BCIs), with immense commercial value. Over the past two decades, there has been a fundamental shift in the trend of MI-EEG classifiers, resulting in a gradual increase in their performance. The emergence of Tensor-CSPNet, the first geometric deep learning (GDL) framework in BCI research, is attributed to the imperative of characterizing the non-Euclidean nature of signals. Fundamentally, Tensor-CSPNet is a deep learning-based classifier that capitalizes on the second-order statistics of EEGs. In contrast to the conventional approach of utilizing first-order statistics for EEG signals, the utilization of these second-order statistics represents the classical treatment. These statistics provide adequate discriminative information, rendering them suitable for MI-EEG classification. In this study, we introduce another GDL classifier, called Graph-CSPNet, for MI-EEG classification. Graph-CSPNet utilizes graph-based techniques to characterize EEG signals in both the time and frequency domains, realizing the fundamental perspective of time-frequency analysis. The architecture of Graph-CSPNet is further simplified, offering greater flexibility to cope with variable time-frequency resolution for signal segmentation and capturing localized fluctuations. In contrast to Tensor-CSPNet, this approach enables Graph-CSPNet to achieve better results in MI-EEG classification. To evaluate the efficacy of Graph-CSPNet, we utilize five commonly-used publicly available MI-EEG datasets, and it produces near-optimal classification accuracies, winning nine out of eleven subject-specific scenarios. The Python implementation of Graph-CSPNet is available on a GitHub repository https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet.

翻译：运动想象（MI）分类是脑电图（EEG）脑机接口（BCI）领域中备受关注的研究课题，具有巨大的商业价值。过去二十年间，MI-EEG分类器的发展趋势发生了根本性转变，其性能逐步提升。Tensor-CSPNet作为BCI研究中首个几何深度学习（GDL）框架的出现，源于对信号非欧几里得特性进行建模的需求。本质上，Tensor-CSPNet是一种基于深度学习的分类器，利用EEG的二阶统计量。与常规使用EEG一阶统计量的方法不同，采用这些二阶统计量代表了经典的处理方式。此类统计量提供了充分的判别信息，使其适用于MI-EEG分类。在本研究中，我们引入另一种名为Graph-CSPNet的GDL分类器用于MI-EEG分类。Graph-CSPNet利用基于图的技术在时域和频域表征EEG信号，实现了时频分析的基本视角。该架构进一步简化，能更灵活地适应可变的时频分辨率以实现信号分割并捕捉局部波动。相较于Tensor-CSPNet，Graph-CSPNet在MI-EEG分类中取得了更优结果。为评估Graph-CSPNet的有效性，我们使用了五个常用的公开MI-EEG数据集，该模型在十一种受试者特定场景中赢得九项，生成了近乎最优的分类准确率。Graph-CSPNet的Python实现已发布在GitHub仓库https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet。