Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.

Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenarios requiring sustained knowledge transfer, the performance of TL-based models gradually declines as the number of transfers increases-a phenomenon known as catastrophic forgetting. To address this issue, we introduce a novel domain-incremental learning framework for the continual motor imagery (MI) EEG classification. Specifically, to learn and retain common features between subjects, we separate latent representations into subject-invariant and subject-specific features through adversarial training, while also proposing an extensible architecture to preserve features that are easily forgotten. Additionally, we incorporate a memory replay mechanism to reinforce previously acquired knowledge. Through extensive experiments, we demonstrate our framework's effectiveness in mitigating forgetting within the continual MI-EEG classification task.