用自监督方法联合建模脑区、通道、时间，提升脑电情绪与认知分析的准确性
RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning
- 通过自适应功能分区构建动态脑区，实现区域-通道-时间联合建模
- 在多个脑电数据集上达到情绪识别与任务专注度分类新最优表现
- 对缺失通道和跨导联场景鲁棒，适合大规模脑电预训练
情感与认知障碍表现为脑区、通道和时间维度上的分布式、时变脑网络动态，给基于脑电（EEG）/皮层脑电（sEEG）的临床诊断带来挑战。我们提出RECTOR（掩码区域-通道-时间建模），一个端到端自监督框架，突破固定解剖先验，统一实现区域-通道-时间联合表征学习。其核心RECTOR-SA采用分层块稀疏自注意力机制，由自适应功能分区驱动，将静态解剖脑区演化为动态功能脑区。自监督学习由掩码拓扑与表征学习驱动，联合优化三个互补目标：掩码预测建模、拓扑结构建模与跨视图一致性。在多个基准测试中，RECTOR在脑电情绪识别与sEEG任务专注度分类上达到新最优性能。关键的是，其对缺失通道和跨导联泛化能力强，具备在异构脑电/sEEG上进行大规模预训练的潜力，并在脑区与通道层面提供可解释性洞察。
原文摘要 · Abstract (English)
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.
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