NeuroAccord: Time–Space–Frequency Guided Continual Learning for Large EEG Models

EEG foundation models learn transferable representations for diverse decoding tasks. As EEG datasets and paradigms expand, continual learning is needed to acquire new capabilities while retaining performance on earlier tasks. Existing approaches mainly rely on two perspectives as protecting important model parameters and replaying representative historical samples. We observe that protected parameters gradually lose importance for their original tasks as the shared model evolves. Meanwhile, fixed replay buffers cover a diminishing share of historical EEG knowledge amid heterogeneity across tasks and subjects. These observations call for a reference that remains meaningful as parameter roles and sample coverage change. We introduce NeuroAccord, a Time–Space–Frequency (TSF)-guided framework linking physiologically motivated EEG descriptors to foundation-model features. It constructs a TSF basis from current-task descriptors and frozen previous-model features, transfers the teacher's relations among examples, and preserves TSF coupling during adaptation. Across stages, accumulated TSF-related directions constrain feature drift without replaying earlier recordings. In task-incremental experiments spanning nine datasets, six paradigms, and four EEG foundation models, NeuroAccord achieves the highest average anytime accuracy and backward transfer and the lowest forgetting among 18 baselines. These results establish state-of-the-art performance among the evaluated methods and a favorable plasticity–stability balance. Ablation, robustness, and continued-learning analyses further support this balance. More broadly, the findings show the value of grounding evolving model representations in EEG signal structure to accumulate new knowledge while retaining earlier decoding capabilities.