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Brain-Eye Collaborative Camouflaged Target Detection
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AI
DOI：10.1080/10447318.2025.2513031.png)
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Abstract
En 中文
Brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) are widely applied in target detection but suffer from low decoding accuracy. While some methods integrate eye movements to improve localization, they often underutilize EEG’s coarse spatial cues, limiting overall detection effectiveness. We propose a brain-eye collaborative method for camouflaged target detection that integrates EEG and eye movement signals in a coarse-to-fine framework. In the coarse stage, target presence and its image quadrant are determined using brain-eye fusion and contrastive learning on bimodal features. This leverages complementary spatiotemporal information from both modalities to generate a coarse localization region. In the fine stage, eye movement data further refines the target location within this region by identifying high-interest areas. This collaborative approach significantly improves both recognition and localization performance in complex visual search tasks. Our method achieves an F1 score of 83.16% and a balanced accuracy of 85.38%, outperforming existing state-of-the-art methods.
Keywords:
Brain–computer interface (BCI)
camouflaged target detection
rapid serial visual presentation (RSVP)
contrast learning
multimodal interaction
Journal
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1.2W
