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
this https URL.

介绍了一种名为 Graph-CSPNet 的基于图的技术，结合时频分析的基本视角，用于 MI-EEG 分类。与 Tensor-CSPNet 不同，该方法在 MI-EEG 分类方面实现了更好的结果。