让脑电模型持续在线适应，提升实际应用中的稳定性与泛化能力
NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models
- 引入多视角一致性与上下文感知调制，实现持续在线适配
- 在多个脑电数据集上表现优于基线，尤其在分布漂移场景下优势明显
- 适合需要实时调整的脑机接口、神经监测等在线场景
脑电基础模型在跨被试和跨任务中学习通用表征方面展现出巨大潜力。然而，现有方法普遍采用预训练-静态部署范式，存在两大缺陷：(1) 预训练目标与下游任务不匹配；(2) 在线场景中对分布漂移的适应能力有限。本文提出 NeuroOnline，一个统一的在线神经自适应框架，支持持续适应。该框架整合两种互补机制：(1) 多视角一致性学习，通过跨视角对齐促进一致且任务相关的表征；(2) 上下文感知表征调制，利用可学习的上下文提示结合交叉注意力，动态调整表征以适应演化中的数据分布。两者协同实现表征对齐与动态适应。在多个脑电基准测试中，NeuroOnline 在在线设置下持续优于强基线，在分布漂移条件下表现更优。消融实验和敏感性分析进一步验证了各组件的必要性与整体设计的有效性。
原文摘要 · Abstract (English)
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 and task-relevant representations, and (2) context-aware representation modulation, which leverages a learnable context prompt with cross-attention to dynamically adapt representations to evolving data distributions. Together, these mechanisms unify representation alignment and dynamic adaptation. Experiments on multiple EEG benchmarks show that NeuroOnline consistently outperforms strong baselines in online settings, achieving better performance under distribution shifts. Ablation and sensitivity studies further validate the necessity of each component and the effectiveness of the overall design.
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