Artificial Intelligence for Motor Rehabilitation: Neural Interfaces and Intelligent Technologies

Motor impairment following stroke, spinal cord injury, and limb loss remains a major cause of long-term disability worldwide, driving sustained demand for technologies that can support functional recovery and independence. Recent advances in artificial intelligence (AI), particularly deep learning, neural interfaces, and multimodal biosignal analysis, are transforming how clinicians and engineers interpret human intent and motor function from electroencephalography (EEG), electromyography (EMG), functional near-infrared spectroscopy (fNIRS), and other physiological signals. These developments are creating new opportunities for adaptive and personalised rehabilitation.Conventional rehabilitation programmes may offer limited opportunities for continuous, data-driven adaptation to differences in injury severity, recovery trajectory, and neuromuscular activity. This has created growing interest in systems that can process neural and physiological signals in real time and translate them into control commands for robotic, assistive, and prosthetic devices. However, challenges remain in signal robustness, generalisation across users, real-time implementation, explainability, adaptability, and translation into clinical and real-world settings.This Research Topic aims to bring together advances in AI-driven biosignal analysis, brain-computer and myoelectric interfaces, and intelligent technologies for motor rehabilitation. We welcome contributions addressing, but not limited to, the following themes:- AI methods, including deep learning, for neural and myoelectric signal processing and analysis- Design and evaluation of brain-computer and myoelectric interfaces for motor imagery and motor execution in people affected by stroke, spinal cord injury, and other motor impairments- Adaptive and personalised control of rehabilitation robots, exoskeletons, wearable assistive devices, and myoelectric prostheses- Multimodal fusion of EEG, EMG, fNIRS, kinematic, and wearable-sensor data for personalised rehabilitation and clinical decision support- Real-time, embedded, and edge-computing solutions for deploying AI-enabled rehabilitation systems- Clinical validation, digital biomarkers, rehabilitation assessment, outcome prediction, and translational pathways for AI-driven rehabilitation systems- Feedback, virtual and augmented reality, and human-robot interaction approaches that support usability, engagement, and rehabilitation outcomesWe invite Original Research, Reviews, Mini Reviews, and Perspective articles from engineers, computer scientists, neuroscientists, clinicians, rehabilitation scientists, physiotherapists, occupational therapists, and other researchers working at the intersection of AI and motor rehabilitation. By bringing together cross-disciplinary advances, this Research Topic seeks to support the development and translation of effective, accessible, and clinically relevant rehabilitation technologies. Topic editors: Frank Kulwa, Mojisola Grace Asogbon Source: https://www.frontiersin.org/research-topics/84457/artificial-intelligence-for-motor-rehabilitation-neural-interfaces-and-intelligent-technologies