Toward Memory-Efficient Continual Adaptation for MI-EEG Decoding in BCIs
提出一种主体增量持续适应范式，通过可扩展架构和原型一致性正则化实现内存高效适应，在三个基准数据集上验证了其能减少历史样本依赖、稳定解码性能，对开发可扩展且保护隐私的脑机接口系统有价值。
Current noninvasive electroencephalography (EEG)-based brain–computer interface (BCI) systems face a fundamental scalability barrier: they either suffer catastrophic forgetting (CF) when learning from new users or require centralized management and use of sensitive neural data from previous users-making real-world deployment impractical. To address this, we introduce subject-incremental continual adaptation (SI-CA), a novel paradigm that models cross-subject continual learning (CL), where knowledge transfer and limited replay sustain stable performance as new subjects are introduced, enabling continual decoding without forgetting. Building on this paradigm, we propose a novel CL framework that achieves memory-efficient adaptation by integrating an extendable architecture with prototype-based consistency regularization and limited replay to mitigate CF. The effectiveness of our proposed method has been validated on three benchmark EEG-BCI datasets. Experimental results demonstrate that the proposed method can effectively reduce reliance on historical samples during CL, while maintaining stable decoding performance for previously learned individuals and ensuring reliable motor decoding for newly encountered ones. This holds significant importance for the development of scalable, privacy-preserving, and stable neural interface systems.