  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.
