Apr 20, 2026 · cs.CVJ/K move · Enter open · S save
Feixue Shao, Guangze Shi, Xueyu Liu, Yongfei Wu+5
School of Mathematics, Taiyuan University of Technology, Shanxi 030024, China · College of Artificial Intelligence, Taiyuan University of Technology, Shanxi 030024, China · National Human Genetics Resource Center, National Research Institute for Family Planning, Beijing 100000, China · Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100000, China
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.