Plug-and-Play Multi-Domain Fusion Adaptation for Cross-Subject EEG-Based Motor Imagery Classification

Motor imagery (MI) classification in rehabilitation brain-computer interfaces (RBCIs) faces significant challenges due to the variability of electroencephalography (EEG) signals across subjects. Existing methods typically require extensive EEG data collection from each new subject, which is time-consuming and results in poor user experience. To address this issue, this paper decompose MI-EEG into subject-specific private components and shared components common across all subjects, and propose a plug-and-play domain fusion adaptive method (PPMDFA) to handle variability between subjects. In the training phase, PPMDFA introduces a Multi-Domain Fusion Graph Convolutional Network (MDFGCN) module to extract shared and private features from the MI processes of source domain subjects. In the calibration phase, the method constructs private classifiers for the target new subject using the extracted shared features combined with a small amount of labeled data. During testing, PPMDFA leverages the similarity of private components to utilize knowledge from source subjects, thereby enhancing classification accuracy for target subjects' MI. We validated the proposed method on the PhysioNet and LLMBCImotion datasets. Experimental results show that PPMDFA achieves state-of-the-art classification accuracy on both datasets, with rapid adaptation to new subjects using only 20% of the data, reaching accuracies of 73.33% and 61.62%, demonstrating strong generalization ability and robustness.