神经状态预测：阻碍EEG基础模型中的捷径学习
Neural State Prediction: Obstructing Shortcut Learning in EEG Foundation Models
- Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对EEG基础模型依赖位置线索和局部相关性的捷径学习问题，提出神经状态预测（NSP）框架，通过EMA目标编码器、身份残差化和拓扑分离上下文约束预测，在30个下游数据集上评估，全参数微调下宏平衡准确率达63.94，超最强基线2.35个百分点。
AI中文摘要：
EEG基础模型越来越多地使用掩码预测从未标记的录音中学习，但优化这一目标并不能确保可迁移的神经表征。一个核心挑战是，稳定的位置线索和局部相关性可能使掩码区域在不整合分布式神经上下文的情况下即可被预测。为了减少对这种低信息预测路径的依赖，我们引入了神经状态预测（NSP），这是一种潜在预测框架，同时约束预测目标和可用上下文。NSP使用由指数移动平均（EMA）更新的目标编码器来定义潜在监督。身份残差化从目标中移除与通道身份和相对时间相关的加性效应，而拓扑分离的上下文则从可见输入中排除其直接的空间和时间邻域。我们在来自TUEG的220万个EEG片段上预训练NSP，并在涵盖临床诊断、睡眠分期、情绪识别、运动想象、事件相关电位、认知状态解码和语言检索的30个下游数据集上对其进行评估。在EEG-FM-Bench上进行全参数多任务微调时，NSP在14个数据集上达到63.94的宏平衡准确率，超过最强评估基线2.35个百分点。受控组件消融评估了每个机制的贡献，而匹配的上下文控制和保留干预则表征了上下文几何、信号内容和位置信息的作用。联合设计潜在目标及其上下文为从分布式信号结构学习的EEG基础模型提供了一个有前景的方向。
英文摘要：
EEG foundation models increasingly use masked prediction to learn from unlabeled recordings, but optimizing this objective does not ensure transferable neural representations. A central challenge is that stable positional cues and local correlations can make masked regions predictable without integrating distributed neural context. To reduce this reliance on low-information prediction paths, we introduce Neural State Prediction (NSP), a latent-predictive framework that constrains both the prediction target and the available context. NSP uses a Target Encoder updated by an exponential moving average (EMA) to define latent supervision. Identity residualization removes additive effects associated with channel identity and relative time from the targets, while topology-separated context excludes their immediate spatial and temporal neighborhood from the visible input. We pretrain NSP on 2.2 million EEG segments from TUEG and evaluate it across 30 downstream datasets spanning clinical diagnosis, sleep staging, emotion recognition, motor imagery, event-related potentials, cognitive-state decoding, and language retrieval. Under full-parameter multi-task fine-tuning on EEG-FM-Bench, NSP achieves 63.94 macro balanced accuracy across 14 datasets, exceeding the strongest evaluated baseline by 2.35 percentage points. Controlled component ablations assess the contribution of each mechanism, while matched context controls and held-out interventions characterize the role of context geometry, signal content, and positional information. Jointly designing latent targets and their context offers a promising direction for EEG foundation models that learn from distributed signal structure.
