NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models

EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks. However, most existing approaches follow a pretraining–static deployment paradigm, which suffers from two key limitations: (1) misalignment between pretraining objectives and downstream tasks, and (2) limited adaptability to distribution shifts in online settings. We propose Online Neural Adaptation (NeuroOnline), a unified framework that enables continuous adaptation in online scenarios. NeuroOnline integrates two complementary mechanisms: (1) multi-view consistency learning, which enforces cross-view alignment to promote consistent representations and improve robustness to distribution shifts; (2) context-aware representation modulation, which uses cross-attention between a learnable prompt and EEG representations to enable adaptive modulation under evolving data distributions. Together, these mechanisms unify representation alignment and dynamic adaptation. Experiments on multiple EEG benchmarks demonstrate that NeuroOnline consistently outperforms strong baselines in online settings and achieves robust performance under distribution shifts. Extensive experimental results further validate the effectiveness and robustness of the proposed framework. The code is available at https://anonymous.4open.science/r/NeuroOnline-25FB.