模仿人眼处理机制，提升脑信号转图像的准确率。
Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decoding
- 构建仿人眼的动态静态转换流程，用互信息调节模糊度。
- 在两个公开数据集上零样本图像检索性能提升8%-9%。
- 适合脑机接口和神经科学领域研究者参考使用。
视觉解码神经生理信号是脑机接口与计算神经科学的关键挑战。现有方法常受神经与视觉模态间系统性与随机性差异限制，忽视了人眼视觉系统（HVS）的内在计算机制。为此，我们提出脑启发捕获（BI-Cap），一种模拟人眼感知过程的神经形态视觉仿真范式，通过模拟HVS处理过程对齐两类模态。具体地，构建包含四个生物合理动态与静态变换的神经形态流水线，并结合互信息（MI）引导的动态模糊调节，模拟自适应视觉处理。为缓解神经活动固有的非平稳性，引入证据驱动的潜在空间表示，显式建模不确定性，确保鲁棒的神经嵌入。在两个公开基准上的零样本脑-图像检索任务中，BI-Cap显著优于当前最优方法，相对提升分别为9.2%和8.0%。源代码已发布于GitHub：https://github.com/flysnow1024/BI-Cap。
原文摘要 · Abstract (English)
Visual decoding of neurophysiological signals is a critical challenge for brain-computer interfaces (BCIs) and computational neuroscience. However, current approaches are often constrained by the systematic and stochastic gaps between neural and visual modalities, largely neglecting the intrinsic computational mechanisms of the Human Visual System (HVS). To address this, we propose Brain-Inspired Capture (BI-Cap), a neuromimetic perceptual simulation paradigm that aligns these modalities by emulating HVS processing. Specifically, we construct a neuromimetic pipeline comprising four biologically plausible dynamic and static transformations, coupled with Mutual Information (MI)-guided dynamic blur regulation to simulate adaptive visual processing. Furthermore, to mitigate the inherent non-stationarity of neural activity, we introduce an evidence-driven latent space representation. This formulation explicitly models uncertainty, thereby ensuring robust neural embeddings. Extensive evaluations on zero-shot brain-to-image retrieval across two public benchmarks demonstrate that BI-Cap substantially outperforms state-of-the-art methods, achieving relative gains of 9.2\% and 8.0\%, respectively. We have released the source code on GitHub through the link https://github.com/flysnow1024/BI-Cap.
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