Target detection plays a critical role in daily applications. While traditional computer vision techniques have demonstrated certain effectiveness in remote sensing image target detection, their performance is constrained by dependence on prior knowledge and limitations in target feature representation. Notably, their detection performance deteriorates significantly under factors such as varying illumination, target occlusion, and limited sample sizes. In contrast, the human brain exhibits remarkable capabilities for target identification in complex environments.This study investigates an electroencephalogram(EEG)-based rapid serial visual presentation(RSVP) method for target detection in remote sensing imagery. By integrating brain-computer interface(BCI) technology, the proposed approach achieves efficient and accurate target detection. Furthermore, we present a novel neural network architecture(MCAMEEGNet)—which combines multi-scale convolution networks, channel attention mechanisms, and a compact Electroencephalography network(EEGNet). Experimental results demonstrate that this model achieves significant improvements in event-related potential(ERP)-based RSVP target recognition, attaining an accuracy of 94.77% on the test set. The research establishes a novel technical framework for EEG-based remote sensing image target detection, offering both theoretical insights and practical applications.

Qianqian Yang, Wei Wang. RSVP Target Detection Based on MCAMEEGNet. The Frontiers of Society, Science and Technology (2025), Vol. 7, Issue 4: 58-66. https://doi.org/10.25236/FSST.2025.070408.

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