Brain-Computer Interfaces in Rehabilitation: Implementation Models and Future Perspectives

医学 康复 物理医学与康复 脑-机接口 人机交互 认知科学 物理疗法 精神科 脑电图 心理学 计算机科学
作者
Raktim Swarnakar
出处
期刊:Cureus [Cureus, Inc.]
标识
DOI:10.7759/cureus.88873
摘要

Brain-computer interfaces (BCIs) represent an emerging advancement in rehabilitation, enabling direct communication between the brain and external devices to aid recovery in individuals with neurological impairments. BCIs can be classified into invasive, semi-invasive, non-invasive, or hybrid types. By interpreting neural signals and converting them into control commands, BCIs can bypass damaged pathways, offering therapeutic potential for conditions such as stroke, spinal cord injury, traumatic brain injury, and neurodegenerative diseases such as amyotrophic lateral sclerosis. BCIs' current applications, such as motor restoration via robotic exoskeletons and functional electrical stimulation, cognitive enhancement through neurofeedback and attention training, and communication tools for individuals with severe physical limitations, are largely being explored within research settings and are not yet part of routine clinical practice. Advances in EEG signal acquisition, machine learning, wearable and wireless systems, and integration with virtual reality are enhancing the clinical utility of BCIs by improving accuracy, adaptability, and usability. However, widespread clinical adoption faces challenges, including signal variability, training complexity, data privacy, and ethical and regulatory issues. Ethical challenges in BCI include issues related to the ownership and misuse of brain data, risks of neural interference, threats to autonomy and personal identity, as well as concerns around data privacy, user consent, emotional manipulation, and accountability in neural interventions. In this context, this editorial has also proposed one model (NEURO model checklist) for BCI implementation in rehabilitation. The future of BCIs in rehabilitation lies in developing personalized, closed-loop, and home-based systems, enabled by interdisciplinary collaboration among clinicians, engineers, neuroscientists, and policymakers. With continued research and ethical implementation, BCIs have the potential to transform neurorehabilitation and greatly enhance patient outcomes and quality of life.
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