Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time, challenging robust representation learning from EEG/sEEG for clinical diagnosis. We propose RECTOR (Masked Region-Channel-Temporal Modeling), an end-to-end self-supervised framework that unifies joint region-channel-temporal representation learning beyond fixed anatomical priors. At its core, RECTOR-SA is a hierarchical, block-sparse self-attention induced by Adaptive Functional Partitioning that evolves region structures from static anatomical definitions to adaptive functional regions. The self-supervision is driven by Masked Topology and Representation Learning, which jointly optimizes three complementary objectives: Masked Predictive Modeling, Topological Structure Modeling, and Cross-View Consistency. Across diverse benchmarks, RECTOR sets a new state-of-the-art in EEG emotion recognition and sEEG task-engagement classification. Crucially, its strong robustness to missing channels and cross-montage generalization underscores its potential for large-scale pre-training on heterogeneous EEG/sEEG, providing interpretable insights at both region and channel levels.

翻译：情感与认知障碍表现为分布式的、随时间和区域变化的脑网络动态特征，这对基于EEG/sEEG进行稳健表征学习以支持临床诊断构成挑战。我们提出RECTOR（掩蔽区域-通道-时间建模），这是一个端到端的自监督框架，能够在固定解剖先验之外统一进行区域-通道-时间联合表征学习。其核心模块RECTOR-SA是一种由自适应功能划分诱导的分层块稀疏自注意力机制，该机制将区域结构从静态解剖定义演变为自适应功能区域。自监督学习由掩蔽拓扑与表征学习驱动，该机制联合优化三个互补目标：掩蔽预测建模、拓扑结构建模和跨视图一致性。在多个基准测试中，RECTOR在EEG情绪识别和sEEG任务参与分类任务上均取得最先进成果。尤为关键的是，其在缺失通道和跨导联泛化方面表现出的强鲁棒性，彰显了其在异质EEG/sEEG大规模预训练中的潜力，并提供了区域与通道层面的可解释性洞见。