Meta-MMD Fusion: Enhancing Cross-Subject Motor Imagery Classification

Motor imagery (MI) is a widely used paradigm in brain-computer interfaces (BCIs). Despite recent advancements, MI classification still faces challenges such as limited data availability and poor performance for new users. In particular, feature alignment based on deep learning in zero-calibration cross-subject frameworks remains inadequately explored. To address these issues, we propose the Meta-MMD, a novel cross-subject MI classification method that integrates meta-learning and maximum mean discrepancy (MMD) strategies. Our method minimizes the discrepancy between support and query distributions within each learning task, enhancing robustness and generalization. Experimental evaluations on two public datasets, BCI Competition IV-2a and BCI Competition IV-2b, were conducted using two backbone networks, EEGNet and DeepConvNet.Based on EEGNet, the accuracies are 69.27% and 79.22%, respectively. Based on DeepConvNet, the accuracies are 67.36% and 78.17%, respectively. Our proposed method outperforms the current state-of-the-art methods. The effectiveness of our method was thus demonstrated.