Brain-computer interface (BCIs) technology is an advanced innovation that facilitates direct communication between the brain and external devices, effectively bypassing damaged neuromuscular pathways. By promoting neuroplasticity through real-time feedback during motor imagery tasks, BCIs enhance motor recovery without requiring actual movement, which is especially beneficial for individuals with severe motor impairments. This paper examines the application of BCI systems in medical rehabilitation, with a focus on enhancing motor function recovery and communication for patients affected by stroke, spinal cord injuries, and neuromuscular disorders such as ALS. However, BCIs still face significant challenges, including low signal quality, user fatigue, and variability in neural patterns. The study concludes by discussing current limitations and proposing future directions such as adaptive learning algorithms, improved signal acquisition methods, and greater personalization. These advances are essential for maximizing BCI effectiveness, expanding its clinical adoption, and improving the quality of life for individuals with severe motor impairments.
Yushuang Deng (Thu,) studied this question.