A general model based on Riemannian manifold for stable decoding movement trajectory from ECoG signals

Decoding continuous 3D hand trajectories from electrocorticographic (ECoG) signals holds potential for brain-computer interface (BCI) applications. However, inter-session variability poses a major challenge for generalization. In this study, we propose a framework that leverages Riemannian-based feature extraction combined with stacked long short-term memory (LSTM) network to enable transfer learning across multiple sessions. ECoG recordings from five monkeys performing reaching tasks are considered. Spatial cross-frequency covariance matrices are computed over the brain area for each of 10 frequency band power and projected onto a Riemannian manifold to extract features which are invariant to session variability. These features and spectral feature are then used to train staked LSTM network. The results show that the proposed method achieves a stable cross-session performance and outperforms baseline models which are trained on frequency features. These findings highlight the potential of combining geometric features with temporal deep learning models for generalized decoding in translational BCI systems.