Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding

• URL: http://arxiv.org/abs/2605.04680v1
• Date: Wed, 06 May 2026 09:31:35 GMT
• Title: Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding
• Abstract summary: EEG-based visual neural decoding aims to align neural responses with visual stimuli for tasks such as image retrieval.<n>We propose MB2L, a Multi-Level Bidirectional Biomimetic Learning framework that incorporates structured physiological biases into representation learning.<n>Experiments show MB2L achieves 80.5% Top-1 and 97.6% Top-5 accuracy on zero-shot EEG-to-image retrieval.
• Score: 10.0396148246889
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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