RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning

• URL: http://arxiv.org/abs/2606.15278v1
• Date: Sat, 13 Jun 2026 12:33:59 GMT
• Title: RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning
• Abstract summary: Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time.<n>We propose RECTOR, an end-to-end self-supervised framework that unifies joint region-channel-temporal representation learning.<n>Across diverse benchmarks, RECTOR sets a new state-of-the-art in EEG emotion recognition and sEEG task-engagement classification.
• Score: 3.249403472775399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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