用生物启发方法提升脑电图像检索准确率
Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding
- 引入视网膜映射先验和分层视觉特征提取，模拟大脑处理过程
- 零样本图像检索达80.5%准确率，跨被试泛化能力强
- 适合脑机接口、神经解码领域研究者参考
基于脑电的视觉神经解码旨在将神经反应与视觉刺激对齐，用于图像检索等任务。然而，配对数据有限，且高保真数字图像与生物视觉感知之间存在根本性差异——受视网膜映射和个体神经解剖结构影响而失真，严重阻碍跨模态对齐。为此，我们提出MB2L框架，通过融入结构化生理归纳偏置来改进表征学习。具体地，设计了带有视觉先验的自适应模糊模块，根据视网膜映射先验重加权视觉输入，缓解感知-结构不匹配；进一步提出生物启发视觉特征提取模块，学习符合层级皮层加工过程的多层级视觉表征，增强跨被试一致性编码。上述模块通过多层级双向对比学习联合优化，在共享语义空间中实现脑电信号与视觉特征的双向对齐。实验表明，MB2L在零样本脑电到图像检索任务上达到80.5%的Top-1准确率和97.6%的Top-5准确率，显著优于现有方法，并展现出强跨被试与实验设置的泛化能力。
原文摘要 · Abstract (English)
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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