EEG-Based Brain-Computer Interface Control for Lower-Limb Rehabilitation Robots: A Focused Review

Abstract

Electroencephalography-based brain-computer interfaces (EEG-based BCIs) provide a non-invasive pathway for incorporating voluntary neural activity into lower-limb rehabilitation robots, exoskeletons, robotic orthoses, and gaittraining systems. This focused review examines the MI-based brain-robot rehabilitation loop, including lower-limb intention decoding, high-level command generation, robot or gait-device interaction, and closed-loop feedback. Evidence is interpreted across four categories: offline lower-limb MI decoding, online BCI demonstrations, lower-limb device integration, and patient-oriented or clinical evaluation. Representative studies support the technical feasibility of decoding lower-limb motor imagery (MI) and using selected BCI outputs in virtual-reality, exoskeleton, and treadmill systems. However, the evidence remains dominated by offline analyses and small proof-of-concept studies, with limited standardized clinical outcomes. Hybrid sensing and multimodal feedback have been explored as complementary strategies, but their value in physical lower-limb rehabilitation requires direct online and patient-oriented validation. The review therefore distinguishes transferable decoding advances from direct rehabilitation evidence and identifies priorities for safe and clinically meaningful system development.