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

Sensors, 2024 · DOI: 10.3390/s24227125 · Published: November 6, 2024

Neurology Neurorehabilitation Research Methodology & Design

Simple Explanation

This study analyzes global research trends regarding EEG-based Brain-Computer Interfaces (BCIs) in rehabilitation between 2013 and 2023. It looks at research articles focusing on technological advancements, effectiveness, and advancements in clinical rehabilitation. The study uses data from Web of Science and bibliometric tools to identify publication trends, geographic distribution, keyword co-occurrences, and collaboration networks in the field. The goal is to enhance the understanding of how BCIs can be integrated into rehabilitation programs more effectively and to emphasize the need for further research to optimize their use and maximize benefits for patients.

Study Duration

10 Years

Participants

Not specified

Evidence Level

Not specified

Key Findings

1
EEG-BCI research has seen a rapid increase, peaking in 2022, primarily focusing on motor and sensory rehabilitation.
2
EEG remains the most commonly used method, with Asia, Europe, and North America making substantial contributions to the research.
3
There is growing interest in using BCIs for mental health and integrating artificial intelligence, especially machine learning, to improve system accuracy and adaptability.

Research Summary

This study provides a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023, focusing on primary research and review articles. The analysis reveals a rapid increase in EEG-BCI research, particularly in motor and sensory rehabilitation, with significant contributions from Asia, Europe, and North America. The study emphasizes the importance of expanding global participation, improving system efficiency through AI integration, and prioritizing ethical considerations to ensure inclusive development and use of BCI technologies.

Practical Implications

Global Collaboration

Promote international collaboration, especially with underrepresented regions like Latin America, to broaden the research and application of EEG-BCIs.

AI Integration

Incorporate artificial intelligence and machine learning to enhance the accuracy, adaptability, and efficiency of EEG-BCI systems.

Ethical Guidelines

Prioritize ethical considerations, including data privacy, transparency, and equitable access, to ensure responsible and inclusive use of BCI technologies.

Study Limitations

1
The study may have excluded relevant publications not indexed in the databases used.
2
The focus on English-language publications may have restricted the inclusion of significant research in other languages.
3
System inefficiencies and slow learning curves remain challenges.

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