A Multiscale Convolutional Fusion Riemannian Manifold Learning Method for EEG Decoding

Deep learning-based methods have been widely adopted for decoding motor imagery (MI) EEG signals. However, due to the low signal-to-noise ratio, non-stationary characteristics, and spatial complexity of EEG data, conventional deep learning approaches operating in Euclidean space often struggle to capture the intrinsic spatial coupling structures of EEG signals. Moreover, the limited size of EEG datasets frequently leads to overfitting. To address these challenges, this study proposes a learning framework that integrates multi-scale convolution with Riemannian manifold learning. Specifically, multiple convolutional kernels of varying sizes are employed to extract detailed features from both low-frequency and high-frequency components of EEG signals. These features are then stacked into tensors to enable comprehensive multi-granularity representation from limited data. The stacked multi-scale features are subsequently projected onto a Riemannian manifold to extract global inter-channel coupling information. In addition, a manifold-based encoder-decoder architecture is employed to promote feature reuse, thereby enhancing the model’s ability to represent nonlinear data structures. The proposed model was evaluated on the BCIC-IV-2a and BCIC-IV-2b datasets, achieving average decoding accuracies of 82.32% and 82.03%, respectively. Experimental results demonstrate that the proposed method outperforms traditional approaches in terms of classification accuracy and robustness.