Learning Brain-Aligned Visual Representation with Adaptive Temporal Recalibration for EEG-Based Visual Decoding

Decoding visual stimuli from brain activity is a central task in understanding visual perception and advancing BCI applications. Existing methods typically align EEG signals with frozen pretrained visual embeddings and process temporal activity uniformly. This paradigm suffers from two limitations: a representational mismatch between machine vision and human perception, and temporal sparsity of decodable visual information in EEG. Together, these yield poorly aligned decoders that struggle with zero-shot generalization. To address these challenges, we propose BEAT, a Brain-aligned visual Encoder with Adaptive Temporal recalibration. To mitigate the representational mismatch, we introduce a dual-alignment strategy, integrating multi-teacher visual distillation with cross-modal contrastive learning. Together, these objectives guide the visual encoder to learn semantically meaningful and neurally aligned representations. To address temporal sparsity, an adaptive temporal recalibration module emphasizes visually informative signal intervals while suppressing noisy ones via temporal contextualization. This lightweight calibrator could be readily applied to various backbones in a plug-and-play manner. Experiments demonstrate state-of-the-art zero-shot brain-to-image retrieval, while reconstruction results further validate the utility of the learned representations.