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The paper introduces MB2L, a Multi-Level Bidirectional Biomimetic Learning framework, to improve EEG-based visual decoding by incorporating physiological inductive biases. MB2L uses Adaptive Blur with Visual Priors and Biomimetic Visual Feature Extraction to address the mismatch between digital images and biological visual perception. By jointly optimizing these modules with Multi-level Bidirectional Contrastive Learning, the framework achieves state-of-the-art zero-shot EEG-to-image retrieval accuracy, demonstrating strong generalization.
Achieve 80.5% Top-1 accuracy in zero-shot EEG-to-image retrieval by mimicking the human visual system, substantially outperforming existing methods.
EEG-based visual neural decoding aims to align neural responses with visual stimuli for tasks such as image retrieval. However, limited paired data and a fundamental mismatch between high-fidelity digital images and biological visual perception - distorted by retinotopic mapping and subject-specific neuroanatomy - severely impede cross-modal alignment. To address this, we propose MB2L, a Multi-Level Bidirectional Biomimetic Learning framework that incorporates structured physiological inductive biases into representation learning. Specifically, we propose Adaptive Blur with Visual Priors to mitigate perceptual-structural mismatch by reweighting visual inputs according to retinotopic priors. We further propose Biomimetic Visual Feature Extraction to learn multi-level visual representations consistent with hierarchical cortical processing, enhancing subject-invariant encoding. These modules are jointly optimized via Multi-level Bidirectional Contrastive Learning, which aligns EEG and visual features in a shared semantic space through bidirectional contrastive objectives. Experiments show MB2L achieves 80.5% Top-1 and 97.6% Top-5 accuracy on zero-shot EEG-to-image retrieval, significantly outperforming prior methods and demonstrating strong generalization across subjects and experimental settings.
