Multi-source Dictionary Transfer Learning for few-shot motor imagery EEG classification Multi-source Dictionary Transfer Learning for Few-shot

• Published in: Signal, image and video processing Vol. 19; no. 11
• Main Authors: Li, Xiaoyu, She, Qingshan, Cai, Yinhao, Fang, Feng, Meng, Ming, Zhang, Yingchun
• Format: Journal Article
• Language: English
• Published: London Springer London 30.07.2025
• Subjects: Computer Imaging; Computer Science; Image Processing and Computer Vision; Multimedia Information Systems; Original Paper; Pattern Recognition and Graphics; Signal,Image and Speech Processing; Vision; Riemannian geometry; Brain-computer interface (BCI); Electroencephalogram (EEG); Multi-source semi-supervised transfer learning; Dictionary learning
• ISSN: 1863-1703; 1863-1711
• DOI: 10.1007/s11760-025-04532-7

Motor imagery electroencephalography (MI-EEG) is a critical brain-computer interface paradigm for direct control of external devices. However, cross-subject MI-EEG classification faces two major challenges: (1) the complex dynamic nature and low signal-to-noise ratio of MI-EEG makes it difficult to extract high-level features that have enough class-discrimination; (2) the scarcity of labeled target-subject data severely limits model generalizability. To address the problems, this paper proposes a novel method called multi-source dictionary transfer learning (MSDTL). Through Fisher-embedded dictionary atoms and coefficients, MSDTL enhances intraclass compactness while maximizing interclass separation. Simultaneously, maximum mean discrepancy-based adaptation aligns marginal and conditional distributions across domains. Extensive experiments on four MI-EEG datasets demonstrate MSDTL’s superiority. It achieves 84.36% average accuracy on the BCI Competition IV Dataset I under several few-shot settings, outperforming state-of-the-art methods by 0.2%-4.06%. While current implementation focuses on offline analysis, future work will extend to real-time and semi-supervised scenarios with unlabeled data.