让脑电图还原图像更准：根据注意力自动选焦点，提升跨人检索效果。
SIMON: Saliency-aware Integrative Multi-view Object-centric Neural Decoding
- 用显著性预测和分割选择注视点，动态生成聚焦关键物体的视觉图。
- 在THINGS-EEG数据集上，跨被试检索准确率达19.6%，创纪录。
- 适合做脑机接口、神经影像与视觉认知交叉研究的人参考。
近期脑电图转图像检索方法利用预训练视觉编码器和类似中央凹的先验知识，但通常假设固定中心视角，与内容驱动的人类注意力不符，导致视觉特征与脑电响应间存在几何-语义错位。本文提出SIMON，一种零样本脑电图转图像检索的显著性感知多视角框架。SIMON结合前景分割与显著性预测，通过显著性感知采样（SAS）选择注视中心，生成突出信息区域、抑制背景干扰的中央凹视图。在THINGS-EEG数据集上，SIMON在被试内和被试间设置中均达到当前最优性能，平均Top-1准确率分别达69.7%和19.6%，持续优于近期先进基线模型。对采样粒度、脑电通道拓扑及视觉/脑部编码器主干的分析进一步验证了显著性感知多视角融合的鲁棒性。代码与模型已公开于https://github.com/simonlink666/SIMON。
原文摘要 · Abstract (English)
Recent EEG-to-image retrieval methods leverage pretrained vision encoders and foveation-inspired priors, but typically assume a fixed, center-focused view. This center bias conflicts with content-driven human attention, creating a geometric-semantic dissociation between visual features and EEG responses. We propose SIMON, a saliency-aware multi-view framework for zero-shot EEG-to-image retrieval. SIMON combines foreground segmentation and saliency prediction to select fixation centers via Saliency-Aware Sampling (SAS), then generates foveated views that emphasize informative object regions while suppressing background clutter. On THINGS-EEG, SIMON achieves state-of-the-art performance in both intra-subject and inter-subject settings, reaching an average Top-1 accuracy of 69.7% and 19.6%, respectively, consistently outperforming recent competitive baselines. Analyses across sampling granularity, EEG channel topology, and visual/brain encoder backbones further support the robustness of saliency-aware multi-view integration. Our code and models are publicly available at https://github.com/simonlink666/SIMON.
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