NeuroAction: Aligning EEG to Discrete Motion Primitives for Continuous Motion Decoding

EEG-to-motion decoding remains challenging because noisy scalp EEG is difficult to map directly to continuous kinematics. We present NeuroAction, a framework that aligns EEG representations to discrete motion primitives for continuous motion decoding across heterogeneous paradigms, including full-body free movement, lower-limb treadmill locomotion, and upper-limb freewill reaching and grasping. A VQ-based motion prior discretizes continuous motion into reusable primitives that serve as both alignment prototypes and intermediate prediction targets. Prototypical and supervised contrastive objectives align EEG embeddings to motion-derived prototypes and organize them according to primitive assignments. An E2M Transformer predicts motion-code sequences from the learned EEG representations, while a final geometric refinement stage improves continuous trajectory reconstruction through motion-level training. Under a last-session test protocol, NeuroAction achieves the best mean reconstruction performance among the compared EEG decoding baselines across all three datasets, with the clearest gains on full-body motion in both reconstruction accuracy and distribution-level quality. Multi-subject ablations further support the contributions of EEG-to-primitive alignment, token prediction, and refinement. Code will be released publicly upon acceptance.