Stroke remains a leading cause of adult disability, leaving most survivors enduring persistent motor deficits. Traditional rehabilitation often yields some recovery initially, yet soon plateaus, especially in severely impaired patients. Braincomputer interfaces (BCIs) decode cortical signals (e.g., EEG, ECoG, fNIRS) with patternrecognition algorithms to create closedloop systems that deliver realtime feedback. Common paradigms include motor imagery or attemptedmovement tasks, functional electrical stimulation (BCIFES), robotic exoskeleton control and virtualreality training. There is much evidence from randomized controlled trials and meta-analyses supporting BCI-based interventions validity and effectiveness in both motor improvement and enhancing neuroplasticity compared to conventional therapy. Despite this potential, BCIs have usability problems (such as long calibration times and user fatigue). In addition, technical difficulties like noisy and nonstationary signals, limited bandwidth, complicated setup requirements, and ethical issues regarding both informed consent and data privacy do exist. Overcoming all the restrictions mentioned above and fulfilling the clinical potential of BCIs is the current ongoing goal. This review aims to synthesize current evidence, highlight key technological and clinical challenges, and propose strategic directions for translating BCIs into routine post-stroke rehabilitation.
Yao Yao (Wed,) studied this question.