Dynamic multi-prototype guided domain incremental learning for Electroencephalogram-based disease classification

Electroencephalogram (EEG) plays a pivotal role in the early screening, clinical diagnosis, and prognostic evaluation of neurological disorders. Although EEG-based classification algorithms have achieved remarkable progress in recent years, existing models are primarily designed for static offline scenarios and struggle to adapt to the dynamic characteristics of evolving data distributions over time in clinical settings. While continual learning offers a potential solution, the significant inter-individual variability, non-stationarity, and temporal heterogeneity of EEG signals pose challenges to existing continual learning methods in terms of model adaptability, stability, and the balance between old and new knowledge. To address these issues, this paper proposes a dynamic multi-prototype guided domain-incremental learning method for continual EEG series classification, which employs an evolvable multi-prototype representation guidance mechanism to steer the model. Specifically, we first design a multi-prototype representation strategy that maintains multiple prototypes per class and integrates momentum updates with similarity gating mechanisms to achieve continuous optimization of prototype representations, thereby precisely capturing the dynamic intra-class distribution evolution. Next, we adopt a decoupled training framework for the feature extractor and classifier, leveraging prototype-guided mechanisms to encourage the feature extractor to learn stable inter-task shared representations. Finally, we construct a nearest prototype contrastive loss function to enhance the model’s discriminative capability for decision boundaries and feature structures by optimizing intra-class compactness and inter-class separability. Extensive experimental evaluations on four benchmark datasets demonstrate the effectiveness and efficiency of our proposed method.