Research progress and application of Brain-Computer Interfaces in the diagnosis and treatment of neurological diseases

Brain-Computer Interfaces (BCI) have shown significant potential in the diagnosis, treatment, and prognosis evaluation of neurological diseases. BCI can identify signal patterns related to specific diseases by analyzing EEG signals, achieving early diagnosis and classification. For instance, in diagnosing Parkinson's disease, BCI detects EEG signals associated with movement disorders, aiding clinical judgment and improving diagnostic accuracy. In addition, this work also explores the application of brain computer interfaces in the treatment of neurological diseases. BCI serves as a non-pharmacological intervention to improve symptoms and promote rehabilitation by regulating EEG activity in real-time. In treating Alzheimer's disease, BCI enhances cognitive functions and memory through neural feedback mechanisms. Furthermore, BCI can assess prognosis by monitoring EEG signals to evaluate patient condition and rehabilitation progress, allowing for treatment adjustments and better outcomes. Future prospects include joint diagnosis and treatment systems, multimodal fusion, virtual reality simulations, reinforcement learning, and EEG entropy analysis. However, challenges such as signal interpretation, individual differences, data collection, and hardware limitations remain. Enhancing EEG accuracy, developing portable BCI systems, and improving hardware performance are crucial for advancing BCI in prognosis assessment. We hope to provide reference for further research and application of BCI in the diagnosis, treatment, and prognosis evaluation of neurological diseases.