Clinical practice of Brain–Machine interfaces in neurological disorders - Kaishan Wang; Penghu Wei; Yongzhi Shan; Guoguang Zhao (EngMedicine)
Neurological disorders, such as Parkinson’s disease, stroke, and spinal cord injury, present significant global health challenges, contributing to high morbidity, mortality, and loss of functional independence for afflicted patients. Brain–machine interfaces (BMIs) have emerged as a transformative technology with promising potential for the diagnosis, treatment, and management of these conditions. By creating a direct communication interface between the brain and external devices, BMIs allow patients with severe neurological impairments to regain partial motor function, engage in nonverbal communication, and restore control over lost physiological functions. This review provides a comprehensive overview of recent clinical advancements in BMI applications for neurological disorders, including motor, consciousness, and affective disorders. This study highlights the utility of BMIs in improving motor and sensory functions, enabling communication between severely disabled patients, and delivering targeted therapeutic interventions. Current challenges such as the complexity of neural signal decoding, ethical considerations, and limited accessibility are critically examined. The review outlines prospective future directions, underscoring the importance of integrating artificial intelligence, machine learning, and multimodal signal processing to enhance the precision, adaptability, and clinical efficacy of brain-machine interface technology.