EEG-AS：基于行为重构的脑电基础模型实例级选择方法
EEG-AS: Instance-Level Foundation Model Selection for EEG Foundation Models via Behavior Reconstruction
- Duke Kunshan University（昆山杜克大学）
- Ankara University（安卡拉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究将脑电基础模型选择转化为实例级算法选择问题，提出EEG-AS框架，通过行为重构高效从七个脑电基础模型中选优，大幅缩小了最优单求解器与神谕上界的差距。
AI中文摘要：
脑电图（EEG）是一种用于测量神经活动的非侵入式技术，已广泛应用于神经科学领域。近期脑电基础模型的进展使其在多种神经解码任务中表现出色，但没有任何单一基础模型能在所有数据集或单个脑电实例上始终取得最佳性能，而实例级模型选择问题在很大程度上尚未被探索。为解决这一局限，我们将脑电基础模型选择问题形式化为实例级算法选择（AS）问题，提出了EEG-AS，这是一个实例级算法选择框架，该框架利用推理可用的潜在脑电嵌入、手工设计的神经生理特征以及一个锚定基础模型来表征每个脑电实例。训练期间，EEG-AS学习从锚定基础模型条件下的特权预测 token 重构不可用的基础模型行为；推理期间，它无需执行整个模型组合即可估计这些行为，从而能高效地从七个脑电基础模型中进行选择。在七个公开脑电基准上的实验表明，EEG-AS大幅缩小了每个实例上的最优单求解器（SBS）与神谕上界之间的差距，这些结果凸显了实例级算法选择在脑电基础模型自适应部署中的有效性。
英文摘要：
Electroencephalography (EEG) is a non-invasive technique for measuring neural activity and has been widely used in neuroscience applications. Recent advances in EEG foundation models have enabled strong performance across diverse neural decoding tasks. However, no single foundation model consistently performs best across datasets or individual EEG instances, while instance-level model selection remains largely unexplored. To address this limitation, we formulate EEG foundation model selection as an instance-level Algorithm Selection (AS) problem. We propose \textbf{EEG-AS}, an instance-level algorithm selection framework that characterizes each EEG instance using inference-available latent EEG embeddings, handcrafted neurophysiological features, and an anchor foundation model. During training, EEG-AS learns to reconstruct unavailable foundation-model behaviors from privileged prediction tokens conditioned on an anchor foundation model, while during inference it estimates these behaviors without executing the entire model portfolio, enabling efficient selection from seven EEG foundation models. Experiments on seven public EEG benchmarks demonstrate that EEG-AS substantially narrows the gap between the Single Best Solver (SBS) and the oracle upper bound for each instance. These results highlight the effectiveness of instance-level AS for adaptive deployment of EEG foundation models.
