Four-Stage Domain Adaptation Transfer Learning for EEG-Based Decoding of Unilateral Upper Limb Motor Imagery

The practical application of Brain–Computer Interface (BCI) technology is frequently challenged by significant inter-individual variability in electroencephalogram (EEG) signals. This variability makes it extremely difficult to decode the brain activity of new subjects using pre-recorded data from previous subjects. To address these issues, this study presents an EEG decoding approach based on four-stage domain generalization. We start by preprocessing the data and then dividing it into source and target domains. The source domain data are then passed through four sequential modules: Feature Extraction, Feature Augmentation, Feature Optimization, and Domain Adaptation, where we adjust the parameters using the source domain loss function. Next, the target domain data go through the same four stages while we fine-tune the parameters together with the domain adaptation loss, ultimately obtaining the decoding results for the target domain. The proposed method achieves the highest classification accuracy of 72.61%, outperforming EEGTransferNet by 7.22% and surpassing all classical and deep learning baselines by improvements ranging from 5.97% to 23.86%. Overall, the proposed method significantly enhances cross-subject generalization in motor imagery decoding, offering practical value for plug-and-play BCI applications.