EEG feature extraction methods in motor imagery-based brain-computer interfaces: a systematic review and network meta-analysis

• Published in: Brain-Apparatus Communication
• Main Authors: Jiahao Cheng, Peng Chen, Yufeng Deng, Yi Luo, Fengyan Chen, Jiwang Ma, Fei Wang, Fen Xu, Sheng Guo, X. San Liang, Tao Zhang
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2025-12-01
• Subjects: Network meta-analysis; brain-computer interface; motor imagery; feature extraction methods; classification
• Online Access: https://www.tandfonline.com/doi/10.1080/27706710.2025.2523303

Background: Brain-computer interfaces (BCIs) enable direct interaction between human cognition and external devices by interpreting brain activity via electroencephalogram (EEG) signals. However, the performance of motor imagery (MI)-based BCIs is often limited by the efficiency and accuracy of EEG feature extraction methods. Challenges such as class separability, noise resilience, and computational complexity. Aim: This study systematically evaluates various feature extraction methods used in MI-based BCIs, with a specific focus on their performance in binary and multi-class classification tasks. Methods: A literature database from resources like Web of Science, PubMed, and Google Scholar, conducting statistical and network meta-analyses. Results: Results highlight a strong preference for hybrid methods for their robustness and versatility. The Common Spatial Pattern (CSP) method remains the most common single method, often integrated into hybrids. The network meta-analysis shows that while wavelet transforms perform well in specific contexts, they struggle with task complexity and variability. Hybrid methods, in contrast, demonstrate superior stability, achieving a surface under the cumulative ranking curve (SUCRA) value of 0.811, making them ideal for high-precision tasks. Conclusion: This study highlights the critical role of effective feature extraction, advocating for the benefits of hybrid methods while underscoring the need to further refine traditional approaches.