Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis

• URL: http://arxiv.org/abs/2211.02641v4
• Date: Sun, 20 Aug 2023 13:05:33 GMT
• Title: Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
• Authors: Ce Ju and Cuntai Guan
• Abstract summary: Motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG) In this study, we amplify the deep learning-based geometric MI-EEG classifiers from the perspective of time-frequency analysis. Graph-CSPNet utilizes novel manifold-valued techniques to capture the EEG features in the time-frequency domain.
• Score: 11.285449381629107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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