Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013-2023)

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-11, Vol.24 (22), p.7125
• Main Authors: Angulo Medina, Ana Sophia, Aguilar Bonilla, Maria Isabel, Rodríguez Giraldo, Ingrid Daniela, Montenegro Palacios, John Fernando, Cáceres Gutiérrez, Danilo Andrés, Liscano, Yamil
• Format: Article
• Language: English
• Subjects: Amyotrophic lateral sclerosis; Analysis; Artificial Intelligence; Bibliometrics; Brain - physiology; Brain research; Brain-Computer Interface (BCI); Brain-Computer Interfaces; cognitive rehabilitation; Collaboration; Electroencephalography; electroencephalography (EEG); Electroencephalography - methods; Humans; Keywords; Machine learning; motor rehabilitation; neurorehabilitation; Neurosciences; Publication output; rehabilitation; Rehabilitation - methods; Science; Spinal cord injuries; Stroke; Trends
• ISSN: 1424-8220; 1424-8220
• 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.