发表机构
Max Planck Institute for Human Cognitive and Brain Sciences; ScaDS.AI Dresden/Leipzig; BIFOLD–Berlin Institute for the Foundations of Learning and Data; Technische Universität Berlin; Hector Fellow Academy（马克斯·普朗克人类认知与脑科学研究所; ScaDS.AI 德累斯顿/莱比锡; BIFOLD–柏林学习与数据基础研究所; 柏林工业大学; 赫克托研究员学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过联合功能与表征分析四种脑电基础模型，发现电极布局鲁棒性由编码器与聚合方式共同决定，且表征与功能鲁棒性可分离，仅输入兼容性不足以保证鲁棒性。
AI 中文摘要
脑电基础模型（EEG-FMs）旨在通过学习理想情况下对数据集特定脑电配置（如电极布局）不变的表示，从而泛化到不同数据集。然而，接受不同电极布局作为输入的脑电基础模型并不能保证表示和预测在不同电极配置下保持稳定，尤其是在训练环境之外。在本工作中，我们通过对四种脑电基础模型进行联合功能与表征分析，研究了不同电极布局的影响，这些模型被选择以涵盖不同的布局处理设计。我们在空间信息通道缩减下，评估了跨受试者静息态睁眼/闭眼和受试者内运动想象分类的嵌入。功能鲁棒性通过线性探针在通道数上的泛化性来测试，而表征鲁棒性则通过受试者内相似性和受试者间几何结构的保持来评估。四种模型表现出不同的鲁棒性特征，且两个轴发生分离：嵌入相似性的大变化不一定伴随探针性能的相当退化，而稳定的嵌入仍可能丢失下游性能。比较同一编码器的两种读出方式进一步表明，聚合而非编码器本身决定了功能鲁棒性：将池化到解剖对齐区域比学习到的全局读出退化更少，尽管前者在构造上就是布局不变的。因此，布局鲁棒性是编码器及其聚合的联合属性，表征其需要表征和功能两个轴。仅输入兼容性不能作为两者的证据。
英文摘要
EEG foundation models (EEG-FMs) are intended to generalize across different datasets by learning representations that, ideally, are invariant to dataset-specific EEG configurations such as electrode montages. However, EEG-FMs that accept different montages as input do not guarantee that representations and predictions remain stable across different electrode configurations, especially outside the training setting. In this work, we investigate the effects of different electrode montages through a joint functional and representational analysis of four EEG foundation models selected to span distinct montage-handling designs. We evaluate embeddings on cross-subject resting-state eyes-open/closed and within-subject motor-imagery classification under spatially informed channel reduction. Functional robustness is tested through the generalizability of linear probes across channel counts, while representational robustness is assessed through within-subject similarity and preservation of between-subject geometry. The four models show distinct robustness profiles, and the two axes dissociate: large changes in embedding similarity need not come with comparable probe degradation, and stable embeddings can still lose downstream performance. Comparing two readouts of the same encoder further shows that aggregation, not the encoder alone, determines functional robustness: pooling into anatomically aligned regions degrades less than a learned global readout, despite being montage-invariant by construction. Montage robustness is therefore a joint property of the encoder and its aggregation, and characterizing it requires both a representational and a functional axis. Input compatibility alone is evidence for neither.
Comments17 pages, 9 figures
