PADA: perturbation-aware differential Riemannian attention for robust motor imagery decoding

Abstract

Introduction Riemannian geometry models have significantly improved the robustness and performance of EEG-based motor imagery decoding and are better suited to small sample sizes; however, their ability to extract nonlinear and spatiotemporal variability features often falls short of Euclidean-based deep learning methods. To address these limitations, we introduce a perturbation-aware differential Riemannian attention mechanism (PADA). Methods PADA develops a window-based learnable Riemannian module that maps informative segments onto the manifold to characterize nonlinear features while preserving local structure, together with a two-stream differential manifold attention mechanism. Tangent-space perturbations are used as a probe for feature instability, and attention weights that are hypersensitive to noise are differentially suppressed to distill robust geometric dependencies. Results On the BCI Competition IV-2a and Zhou2016 datasets, PADA achieved competitive clean-condition accuracies of 81.44% and 92.79%, respectively. Under 50% noise, its accuracy dropped by only 12%, achieving the best classification performance among the compared models. On a self-constructed dataset involving 227 subjects, PADA achieved up to 70.46% binary classification accuracy on the classic hand motor imagery task using only 12 training samples. Discussion These results indicate that PADA provides robust and effective feature learning for noisy and data-limited brain–computer interface applications.