Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decoding

TL;DR

This paper introduces Brain-Inspired Capture (BI-Cap), a neuromimetic framework that emulates the human visual system to improve neural decoding in brain-computer interfaces, demonstrating significant performance gains.

Contribution

The paper presents a novel biologically inspired perceptual simulation pipeline with adaptive processing and uncertainty modeling for enhanced neural decoding.

Findings

Outperforms state-of-the-art zero-shot brain-to-image retrieval methods.

Achieves 9.2 ext{ and }8.0 ext{ relative gains on two benchmarks.

Introduces evidence-driven latent space for robust neural embeddings.

Abstract

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.