Title: RECTOR: Masked Region-Channel-Temporal Modeling for Cognitive Representation Learning
Primary area: applications to neuroscience & cognitive science
Keywords: self-supervised learning, self-attention mechanism, masked modeling, contrastive learning, EEG, sEEG, affective and cognitive disorders

Abstract: Affective and cognitive disorders are characterized by complex, distributed brain network dynamics across distinct functional regions, channels, and time, posing a significant challenge to learning robust representations for clinical diagnosis. We introduce $\textbf{RECTOR}$ (Masked $\textbf{Re}$gion–$\textbf{C}$hannel–$\textbf{T}$emp$\textbf{or}$al Modeling), the first end-to-end, self-supervised brain region modeling framework that unifies region, channel, and temporal representation learning in a single architecture. At its core is $\textbf{RECTOR-SA}$, a novel hierarchical self-attention mechanism that incorporates anatomical priors with data-adaptive gating to efficiently model region-channel-temporal interactions in a sparse, block-wise paradigm. RECTOR further integrates $\textbf{RECTOR-Mask}$, a multi-view masking strategy that generates intra-sample region-channel-temporal blocks. It enables self-supervision driven by $\textbf{NC}^\textbf{2}$$\textbf{-MM}$, our learning objective that synergistically optimizes multi-view masked modeling, block-level contrastive learning, and variance-covariance regularization to learn robust representations. Finally, we introduce $\textbf{RCReg}$, a tailored regularization on region–channel tokens that prevents trivial region features and enables learning of both region-common and channel-specific representations. Across diverse benchmarks, RECTOR sets a new state-of-the-art in EEG emotion recognition and sEEG task-engagement classification. In addition, it achieves superior computational efficiency in spatio-temporal self-attention, demonstrates strong potential for large-scale pre-training, and provides interpretable, multi-view insights into neural representations at both brain region and channel levels.

TL;DR: RECTOR is a novel self-supervised EEG/sEEG framework that models region, channel, and temporal dynamics via a hierarchical self-attention mechanism for cognitive representation learning.