Detecting single-trial P300 from EEG is difficult when only a few labeled trials are available. When attempting to boost a small target set with a large source dataset through transfer learning, cross-dataset shift arises. To address this challenge, we study transfer between two public visual-oddball ERP datasets using five shared electrodes (Fz, Pz, P3, P4, Oz) under a strict small-sample regime (target: 10 trials/subject; source: 80 trials/subject). We introduce Adaptive Split Maximum Mean Discrepancy Training (AS-MMD), which combines (i) a target-weighted loss with warm-up tied to the square root of the source/target size ratio, (ii) Split Batch Normalization (Split-BN) with shared affine parameters and per-domain running statistics, and (iii) a parameter-free logit-level Radial Basis Function kernel Maximum Mean Discrepancy (RBF-MMD) term using the median-bandwidth heuristic. Implemented on an EEG Conformer, AS-MMD is backbone-agnostic and leaves the inference-time model unchanged. Across both transfer directions, it outperforms target-only and pooled training (Active Visual Oddball: accuracy/AUC 0.66/0.74; ERP CORE P3: 0.61/0.65), with gains over pooling significant under corrected paired t-tests. Ablations attribute improvements to all three components.

翻译：当仅有少量标记试次可用时，从脑电信号中检测单试次P300成分具有挑战性。在尝试通过迁移学习利用大型源数据集增强小型目标集时，会出现跨数据集偏移问题。为应对这一挑战，我们在严格的小样本条件下（目标集：每受试者10个试次；源集：每受试者80个试次），使用五个共享电极（Fz, Pz, P3, P4, Oz）研究两个公开视觉oddball事件相关电位数据集间的迁移。我们提出了自适应分割最大均值差异训练方法，该方法整合了三个要素：（i）与源/目标样本量比值的平方根相关联的热身目标加权损失；（ii）具有共享仿射参数及各域独立运行统计量的分割批量归一化；（iii）采用中位数带宽启发式的无参数对数层径向基函数核最大均值差异项。在EEG Conformer模型上实现时，该方法与骨干网络无关且不改变推理阶段的模型结构。在双向迁移实验中，其性能均优于仅使用目标集训练及混合训练策略（Active Visual Oddball数据集：准确率/AUC为0.66/0.74；ERP CORE P3数据集：0.61/0.65），经校正配对t检验证实其相对于混合训练的增益具有统计显著性。消融实验表明所有三个组件均对性能提升有所贡献。