A Mixture-of-Expert Model for Cross-Subject Motor Imagery Decoding

Motor Imagery Brain-Computer Interface (MIBCI) is one of the most widely used BCI paradigms. However, due to large inter-individual variability in EEG signals, EEGbased pattern recognition models face significant challenges in cross-subject generalization. In this study, we posit that although cross-subject MI-BCI is fundamentally a cross-domain task, the population is not homogeneous; instead, latent subpopulations exist in which subjects share more consistent classification boundaries. To exploit this structure, we propose a Mixture-of-Experts (MoE) framework that automatically partitions training trials into latent groups and trains expert classifiers specialized for each group, while simultaneously learning a gating network that assigns test trials to the most suitable expert(s). This design enables the system to adapt to subpopulation structure, mitigate negative transfer from dissimilar subjects, and better model inter-subject heterogeneity. Evaluations on two public MI-EEG datasets (EEGMMIDB and OpenBMI) using k-fold cross-subject protocols demonstrate that our MoE approach significantly improves classification accuracy compared to most baseline models.