Memory-augmented-based meta-learning framework for cross-subject motor imagery classification

• Published in: Biomedical signal processing and control Vol. 110; p. 108246
• Main Authors: Liu, Junxiu, Zhao, Xuanyu, Luo, Yuling, Qin, Sheng, Fu, Qiang, Tan, Hongxiao
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2025
• Subjects: Attention mechanism; Cross-subject; Memory augmentation; Meta-learning; Motor imagery; Memory augmentation; Attention mechanism; Motor imagery; Cross-subject; Meta-learning
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2025.108246

Brain-computer interfaces (BCIs) offer a groundbreaking avenue for facilitating communication between the human brain and external devices. Particularly, motor imagery (MI)-based BCIs have shown potential in various applications such as assistive technologies and rehabilitation. However, the challenge of cross-subject variability remains a significant hurdle for the widespread adoption of BCIs, as it affects the generalization capability of these systems to new subjects. In this work, the memory-augmented-based meta-learning framework is proposed, which integrates the Echo State Network (ESN) with Model-Agnostic Meta-Learning (MAML) to address the issue of cross-subject variability in MI-BCI classification, named as MAgML. The proposed framework utilizes processing power of the parallel ESNs. It captures the rich temporal dynamics of Electroencephalogram (EEG) signals and combines this with attentional mechanisms to enhance prolonged feature acquisition. Additionally, the MAML is employed to quickly adapt to new subjects with minimal calibration. The results demonstrate the effectiveness of them on multiple EEG datasets, and the MAgML outperforms existing methods. On the BCI-2A dataset, the results show MAgML has an improvement with 4.3 % over the best-performing method on 1 shot scenario, and 8.4 % on 20 shots scenario. On the BCI-2B dataset, the improvement ranges from 4.3 % (with 1 shot) to 6.6 % (with 20 shots). The MAgML provides a robust zero-calibration solution for practical and efficient BCI applications.