Brain-computer interface technology for motor rehabilitation in severe stroke: a narrative review

This review examines the application of brain–computer interface (BCI) technology for motor rehabilitation in patients with severe stroke—a population often excluded from conventional therapies due to minimal movement. BCIs establish electronic links between the brain and external devices, enabling motor intention recognition without muscular activity. By pairing neural activation with sensory feedback, these systems promote neuroplasticity and strengthen adaptive motor pathways. Compared with standard therapies, preliminary evidence suggests BCI interventions may facilitate additional motor recovery, though current effect size estimates are limited by small sample sizes, high study heterogeneity, and inherent performance biases. Effective modalities include motor imagery with functional electrical stimulation, robotic-assisted training in virtual environments, and multimodal systems. Despite promising results, challenges persist regarding signal reliability, protocol optimization, patient selection, and cost. Emerging research focuses on integrating artificial intelligence, adaptive closed-loop systems, and portable platforms to enhance clinical feasibility. Interdisciplinary collaboration may help transition BCI technology from experimental use to routine rehabilitation, improving outcomes for severely impaired stroke survivors.