FSDBN：通过动态脑网络实现前景感知的脑电图-视觉对齐

中文摘要 AI 辅助

基于脑电图的视觉解码为解释视觉语义提供了一种非侵入性途径。然而，现有方法往往忽略复杂场景中前景与背景的感知不对称，导致背景干扰和语义错位。脑电图信号具有快速的时间动态和非平稳空间模式，难以捕捉与焦点视觉注意相关的时变脑连接性。为解决这些局限性，我们提出FSDBN，一个用于稳健脑电图-视觉解码的统一框架。FSDBN引入语义一致显著对齐，在联合显著和语义约束下从背景噪声中分离语义相关前景区域。它还采用语义先验动态门控前景融合来自适应调节前景和背景特征的贡献。同时，将脑电图信号建模为自适应时空脑网络，其功能连接动态重组以捕捉对显著前景的神经反应。零样本脑到图像检索实验表明，FSDBN的top-1准确率达到69.0%，top-5准确率达到92.2%，优于先前的最先进方法。

英文摘要

EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics. However, existing methods often overlook the perceptual asymmetry between foreground and background in complex scenes, leading to background interference and semantic misalignment. EEG signals also exhibit rapid temporal dynamics and nonstationary spatial patterns, making it difficult to capture the time-varying brain connectivity associated with focal visual attention. To address these limitations, we propose FSDBN, a unified framework for robust EEG-visual decoding. FSDBN introduces Semantic-Consistent Saliency Alignment to separate semantically relevant foreground regions from background noise under joint saliency and semantic constraints. It further employs Semantic-Prior Dynamic Gating Foreground Fusion to adaptively regulate the contributions of foreground and background features. In parallel, EEG signals are modeled as adaptive spatiotemporal brain networks whose functional connectivity dynamically reorganizes to capture neural responses to salient foregrounds. Experiments on zero-shot brain-to-image retrieval demonstrate that FSDBN achieves 69.0 percent top-1 accuracy and 92.2 percent top-5 accuracy, outperforming previous state-of-the-art methods. Code is available at https://github.com/LiuYiheng1/FSDBN-EEG.

发表机构

• College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（人工智能学院，南京航空航天大学）

机构由 AI 辅助整理，请以论文原文为准。