Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts. We study whether task supervision and architecture alone can learn subject-aligned representations. We replace a shared EEG encoder with subject-specific encoders followed by a common classifier, and compare this hybrid model with standard EEGNet, AttentionBaseNet, and CTNet baselines with Euclidean Alignment (EA) on four motor-imagery datasets. EA improves shared encoders by recentering subject covariances, but the hybrid encoder largely internalises this role: validation-loss curves and latent-distance analyses change little when EA is removed. Subject-specific heads increase class distinctiveness and place each subject close to its own latent manifold, improving most subjects while leaving a method-sensitive subset. These results support subject-specific encoders as a learned alignment mechanism for EEG decoding and identify head selection for unseen subjects as the remaining bottleneck.

翻译：跨受试者脑电图解码有望提供更多训练数据，但也使得神经网络面临强烈的受试者间分布偏移。我们研究了仅凭任务监督和网络架构能否学习到与受试者对齐的表征。将共享的脑电图编码器替换为受试者专属编码器后接公共分类器，并在四个运动想象数据集上将该混合模型与采用欧几里得对齐的标准EEGNet、AttentionBaseNet和CTNet基线进行对比。欧几里得对齐通过重新居中受试者协方差来改善共享编码器性能，但混合编码器在很大程度上内化了这一作用：移除欧几里得对齐后，验证损失曲线和潜在距离分析变化甚微。受试者专属头部增强了类别区分性，使每个受试者接近其自身潜在流形，在改善大多数受试者性能的同时，也暴露出方法敏感的子集。这些结果支持将受试者专属编码器作为脑电图解码的学习对齐机制，并指出对未见受试者进行头部选择是当前存在的瓶颈。