Electroencephalography (EEG) reflects underlying brain states, whose activities are distributed across brain regions and manifest as spatial patterns on the scalp. Learning these spatially structured, state-related patterns requires consistent spatial representations across datasets. However, existing EEG foundation models are typically based on self-attention, which does not preserve location-specific information and struggles to align signals recorded with different channel configurations. Moreover, brain states contain both shared and state-specific regional activity, suggesting that learning neurophysiologically plausible, state-aware representations can complement the shared representations targeted by current models and improve downstream decoding. To address these limitations, we propose BrainPro, a large EEG model that combines a retrieval-based spatial learning mechanism for cross-layout spatial alignment with a brain state-decoupling module that learns both shared and state-specific representations through parallel encoders and region-aware reconstruction. Pre-trained on a large EEG corpus, BrainPro achieves state-of-the-art performance across nine public BCI datasets spanning emotion, motor, speech, stress, mental disease, and attention tasks. Analyses of spatial filters, channel-drop robustness, and encoder contributions further validate the effectiveness of its spatial alignment and state-aware pathways. These results show that BrainPro achieves improved interpretability of learned spatial patterns and produces representations that benefit diverse EEG decoding tasks.

翻译：脑电图（EEG）反映潜在的大脑状态，其活动分布在不同脑区，并在头皮上表现为空间模式。学习这些空间结构化、与状态相关的模式，需要跨数据集保持一致的空间表征。然而，现有的脑电图基础模型通常基于自注意力机制，该机制无法保留特定位置信息，也难以对齐不同通道配置记录的信号。此外，脑状态既包含共享的区域活动，也包含特定状态的区域活动，这表明学习神经生理学上合理的、状态感知的表征，可以补充当前模型所针对的共享表征，并改善下游解码任务。为解决这些局限，我们提出BrainPro，一种大型脑电图模型，它结合了基于检索的空间学习机制以实现跨布局空间对齐，以及一个通过并行编码器和区域感知重建学习共享和特定状态表征的脑状态解耦模块。BrainPro在包含情感、运动、言语、压力、精神疾病和注意力任务在内的九个公开脑机接口（BCI）数据集上进行了预训练，并取得了最先进的性能。对空间滤波器、通道丢失鲁棒性和编码器贡献的分析进一步验证了其空间对齐和状态感知通路的效果。这些结果表明，BrainPro提高了所学空间模式的可解释性，并生成了有益于多种脑电解码任务的表征。