Motor Imagery EEG Signal Classification Method Based on SPD Manifold and Gaussian SPD Manifold Kernel Features

Motor imagery EEG (MI-EEG) signal classification is one of the key challenges in Brain-computer interface (BCI) technology. Currently, MI-EEG signal analysis methods based on Riemannian geometry are usually based on a single symmetric positive definite (SPD) manifold. This approach offers a relatively simple geometric structure. However, it struggles to fully capture the complex geometric characteristics of MI-EEG signals. To address this, this paper introduces the Gaussian SPD manifold in addition to the classical SPD manifold. The method extracts more discriminative kernel features from both manifolds. These features are then fused for improved classification performance. The Gaussian SPD manifold models the traditional SPD manifold with a Gaussian distribution. By doing so, it can more accurately describe the statistical characteristics and variations of MI-EEG signals, compensating for the limitations of the single SPD manifold. The method was validated on the OpenBMI binary classification dataset, achieving an average classification accuracy of $80.8 \%$ with a standard deviation of $10.8 \%$. The approach presented in this paper provides a new perspective for the geometric analysis of MI-EEG signals, allowing for a deeper understanding and analysis of their complex geometric structure.