发表机构
UC San Diego; CNRS（加州大学圣迭戈分校; 法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究证明，在超过约50名被试的队列上，EEG-视频对比编码器的解码性能随被试数对数线性增长且未饱和，其中EEG基础模型初始化比随机初始化扩展效率高1.6倍。
AI 中文摘要
从EEG进行自然视觉解码长期受限于队列规模：现有扩展文献最多涵盖几十名被试，导致先前工作得出结论认为扩展被试队列带来的性能提升微乎其微。我们在比先前工作大一个数量级以上的队列上训练跨模态EEG-视频对比编码器，发现该扩展方向有效但存在起始阈值。当被试数低于S≈50（即先前工作覆盖的全部范围）时，没有任何模型比未训练的编码器有显著改进；高于该阈值时，在拟合的刺激特征探针和无拟合的电影时刻检索中，解码性能随被试数量呈对数线性增长，且在我们的阶梯顶端未见饱和，该增益可迁移至第二部未见过的电影。该扩展方向的效果更多取决于初始化而非容量：从EEG基础模型初始化的编码器，其队列每翻倍时扩展速度约为随机初始化编码器（在两种深度下）的1.6倍，是唯一在阶梯顶端仍能将被试数转化为检索准确率的模型，并且仅消耗少量对齐计算。因此，在共享自然刺激上的被试扩展是扩展EEG基础模型的一个有效且当前尚未饱和的前沿方向。
英文摘要
Naturalistic visual decoding from EEG has long been constrained by cohort size: existing scaling literature caps out at a few dozen subjects, leading prior work to conclude that scaling subject cohorts yields minimal performance gains. Training a cross-modal EEG--video contrastive encoder on a cohort larger by more than an order of magnitude, we find the axis is productive but has an onset. Below $S\approx50$ --- the entirety of the range prior work occupies --- no model improves meaningfully over an untrained encoder; above it, decoding rises log-linearly in subject count with no saturation at the top of our ladder, on a fitted stimulus-feature probe and on fit-free movie-moment retrieval alike, and the gain transfers to a second, unseen film. How far the axis carries then depends on initialisation more than on capacity: an encoder initialised from an EEG foundation model scales ${\sim}1.6\times$ faster per doubling of the cohort than randomly initialised encoders at two depths, is the only one still converting subjects into retrieval accuracy at the top of the ladder, and converges on a fraction of the alignment compute. Subject scaling on shared naturalistic stimuli is thus an effective and currently unsaturated frontier for scaling EEG foundation models.
