arXiv cs.AI· Fan Yin, Chuhang Zheng, Peiliang Gong, Donghai Guan, Qi Zhu·· 2 天前AI 评分22
CAIA：认知引导自适应模糊与信息约束对齐的神经视觉解码框架
Neural Visual Decoding via Cognitive guided Adaptive Blurring and Information Constrained Alignment
AI 导读
研究者提出 CAIA 框架，通过认知引导的自适应模糊机制与信息约束对齐方法提升 EEG 视觉解码性能。该框架在视觉侧模拟选择性注意力降低冗余，在 EEG 侧结合神经振荡先验与信息瓶颈启发的频谱稀疏正则化提升信噪比，并引入分布感知边界校准损失修正离群样本对齐偏差。实验表明 CAIA 在零样本脑到图像检索中显著超越此前方法，提升被试依赖与被试独立场景下的平均 Top-1 和 Top-5 准确率。
正文 · 原文
Abstract:EEG-based visual decoding aims to establish a mapping between neural signals and visual semantics. However, it remains constrained by the dual challenges of severe information granularity mismatch and the low signal-to-noise ratio (SNR) of EEG signals. Existing approaches typically treat visual features as static, ignoring the dynamic selectivity of human vision and the frequency specificity of neural oscillations. To bridge this gap, we propose CAIA, a Cognitive-guided Adaptive blurring with Information-Constrained Alignment framework for Neural-Visual decoding. On the visual side, it simulates selective attention to adaptively reduce redundancy. Meanwhile, on the EEG side, it combines neural oscillation priors with an information-bottleneck-inspired spectral sparsity regularizer to enhance SNR. Specifically, we devise a cognitive-dynamics-based adaptive blurring mechanism that dynamically integrates center-biased and saliency-guided visual cues via cross-modal attention. Furthermore, we introduce a distribution-aware boundary calibration loss to robustly rectify alignment bias caused by outlier samples. Moreover, a cognitively guided information-screening method is proposed to select task-relevant EEG oscillations. Extensive experiments demonstrate that CAIA improves both subject-dependent and subject-independent average Top-1 and Top-5 accuracy in zero-shot brain-to-image retrieval, significantly outperforming prior methods. Our work validates that optimizing visual information density to match neural granularity offers a more interpretable and robust pathway for neural decoding.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.16418 [cs.CV] |
| (or arXiv:2605.16418v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16418 arXiv-issued DOI via DataCite |
Submission history
From: Fan Yin [view email]
[v1]
Thu, 14 May 2026 08:50:29 UTC (3,102 KB)
[v2]
Thu, 15 Oct 2026 11:32:27 UTC (4,911 KB)
来源：arXiv cs.AI · arxiv.org