The Frontiers of Society, Science and Technology, 2025, 7(4); doi: 10.25236/FSST.2025.070408.
Qianqian Yang1, Wei Wang2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
2Navy Medical Center, Navy Military Medical University, 200093, Shanghai, China
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.
Rapid Serial Visual Presentation, Event-Related Potentials, Brain-Computer Interface, Convolutional Neural Network
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.
[1] Xiyu S, Bin Y, Li T, et al. Asynchronous Video Target Detection Based on Single-Trial EEG Signals[J]. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 2020, 28(9): 1931-1943.
[2] RAMADAN R A, VASILAKOS A V, et al. Brain computer interface: control signals review[J]. Neurocomputing, 2017 (223): 26-44.
[3] Zhimin L, Ying Z, Hui G, et al. Multirapid Serial Visual Presentation Framework for EEG-Based Target Detection[J]. BioMed research international, 2017, 20(4): 90-94.
[4] Lees S, Dayan N, Cecotti H, et al. A review of rapid serial visual presentation-based brain–computer interfaces[J]. Journal of Neural Engineering, 2018, 15(2): 021001.
[5] Nima B, Andrey V, et al. Brain activity-based image classification from rapid serial visual presentation[J]. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 2008, 16(5): 432-41.
[6] Barngrover C et al. A brain–computer interface (BCI) for the detection of mine-like objects in sidescan sonar imagery[J]. IEEE transactions on pattern analysis and machine intelligence, 2016, 4(1): 124–39.
[7] Matran-Fernandez A, Poli R. Collaborative brain-computer interfaces for target localisation in rapid serial visual presentation[C]. Computer Science and Electronic Engineering Conference, 2014: 127-132.
[8] Jon T, Laurie G, et al. Real-time measurement of face recognition in rapid serial visual presentation[J]. Frontiers in psychology, 2011, 242.
[9] Haofei W, Yiwen W. Convolutional Neural Network for Target Face Detection using Single-trial EEG Signal[J]. IEEE Engineering in Medicine and Biology Society, 2018, 2008-2011.
[10] Pradeep S, Desney S. Human-Aided Computing: Utilizing Implicit Human Processing to Classify Images[C]. The 26th Annual CHI Conference on Human Factors in Computing Systems (CHI 2008), 2008, 11(1):845-854.
[11] Chris H, Annette S, Premkumar E, et al. High throughput screening for mammography using a human-computer interface with rapid serial visual presentation(RSVP)[J].Univ. of Surrey (United Kingdom);The Royal Surrey County Hospital NHS Trust (United Kingdom);Univ. of California, Santa Barbara (United States);Univ. of Pittsburgh (United States), 2013, 86(73): 3-8.
[12] Hubert C, Axel G. Convolutional neural networks for P300 detection with application to brain-computer interfaces[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(3): 433-45.
[13] Pedram H, Maryam Z, Elham M, et al. An efficient deep learning framework for P300 evoked related potential detection in EEG signal[J]. Computer methods and programs in biomedicine, 2022, 229, 107324-107324.
[14] Ziwei Z, Yangfei L, et al. RSVP Target Classification Algorithm for Multi-Layer Frequency Spatio-Temporal Feature Extraction[J]. Journal of Beijing Institute of Technology, 2024, 44(03): 312-320.
[15] Yuan Z, Zhou Q, Wang B, et al. PSAEEGNet: pyramid squeeze attention mechanism-based CNN for single-trial EEG classification in RSVP task[J]. Frontiers in Human Neuroscience, 2024, 18,1385360-1385360.
[16] Chen H, Wang D, Xu M, et al. A multi-source classification framework with invariant representation reconstruction for dual-target RSVP-BCI tasks in cross-subject scenario[J]. Neurocomputing, 2025, 620,129239-129239.
[17] Lawhern J V, Solon J A, Waytowich R N, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces[J]. Journal of Neural Engineering, 2018, 15(5): 056013.
[18] G. H. Klem, et al. The ten-twenty electrode system of the International Federation[J]. Electroencephalogr Clin Neurophysiol, 1999, 52(1): 3-6.
[19] Hongfei Z, Zehui W, Yinhu Y, et al. An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task[J]. Brain Science Advances, 2022, 8(2): 111-126.
[20] Arnaud D, Scott M. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis[J]. Journal of neuroscience methods, 2004, 134(1): 9-21.
[21] Subasi A, Gursoy I M. EEG signal classification using PCA, ICA, LDA and support vector machines[J]. Expert Systems with Applications, 2010, 37(12): 8659-8666.