FSDBN: Foreground-Aware EEG--Visual Alignment via Dynamic Brain Networks
For researchers in EEG-based visual decoding, FSDBN provides a method to mitigate background interference and capture dynamic brain connectivity, improving retrieval accuracy.
FSDBN addresses background interference and temporal dynamics in EEG-based visual decoding by aligning foreground-aware visual features with dynamic brain networks, achieving 69.0% top-1 and 92.2% top-5 accuracy on zero-shot brain-to-image retrieval, outperforming prior methods.
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.