Grupo de Investigación en Salud Integral (GISI), Departamento Facultad de Salud, Universidad Santiago de Cali, Cali 5183000, Colombia.
Specialization in Internal Medicine, Department of Health, Universidad Santiago de Cali, Cali 5183000, Colombia.
Sensors (Basel). 2024 Nov 6;24(22):7125. doi: 10.3390/s24227125.
EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.
基于脑电图的脑-机接口（BCI）因其非侵入性、易于获取的捕捉大脑活动的能力以及在中风和脊髓损伤等患者中恢复神经功能的能力，在康复领域引起了广泛关注。本研究对 2013 年至 2023 年期间基于脑电图的 BCI 在康复中的全球研究进行了全面的文献计量分析。它侧重于解决技术创新、有效性和临床康复系统改进的主要研究和综述文章。数据来自 Web of Science 等数据库，使用文献计量工具（bibliometrix R）分析了出版物趋势、地理分布、关键词共现和合作网络。结果表明，基于脑电图的 BCI 研究呈快速增长趋势，在 2022 年达到峰值，主要集中在运动和感觉康复上。脑电图仍然是最常用的方法，亚洲、欧洲和北美做出了重大贡献。此外，人们越来越感兴趣地将 BCI 应用于心理健康，并将人工智能（AI），特别是机器学习，应用于提高系统准确性和适应性。然而，仍然存在挑战，例如系统效率低下和学习曲线缓慢。通过采用多模态方法和先进的神经影像学技术，可以解决这些问题。需要进一步研究以验证 EEG-BCI 技术在认知和运动康复中的适用性，特别是考虑到全球脑血管疾病的高患病率。为了推动该领域的发展，必须扩大全球参与度，特别是在拉丁美洲等代表性不足的地区。通过采用多模态方法和人工智能集成来提高系统效率也至关重要。必须优先考虑数据隐私、透明度和公平获取 BCI 技术等伦理问题，以确保这些技术在不同社会经济群体中的包容性开发和使用。
