Riemannian manifold dynamic attention fusion network for motor imagery EEG decoding

A notable challenge encountered by motor imagery decoding algorithms utilizing electroencephalography (EEG) signals is the substantial redundancy and inadequate geometric representation of spatiotemporal features, which stem from the volume conduction effects inherent to the human head. This phenomenon can obscure essential information regarding motor intentions with noise or non-discriminative features. Although traditional decoding models have sought to alleviate redundancy through shallow attention mechanisms or feature selection in Euclidean space, they frequently neglect the intrinsic manifold geometric properties of EEG signals, such as the positive definiteness of covariance matrices. Additionally, static attention weights are often insufficient in dynamically capturing the cross-domain dependencies between spatiotemporal and spectral features. To address these limitations, we propose a novel spatiotemporal dynamic attention fusion network grounded in Riemannian manifolds (ST-MA-SENet) for EEG motor imagery decoding. ST-MA-SENet adeptly assesses the spatiotemporal correlations among EEG features in both Euclidean and Riemannian spaces from a comprehensive perspective, thereby facilitating the selection of a distinctive and effective EEG fusion feature for motor imagery recognition. To evaluate the efficacy of ST-MA-SENet, we conducted experiments utilizing three motor imagery datasets (BCI IV 2a, BCI IV 2b, HGD), and the results demonstrate that ST-MA-SENet represents a highly promising approach for EEG signal decoding.