ArticleCyborg and bionic systems (Washington, D.C.)2024
Low-Quality Video Target Detection Based on EEG Signal Using Eye Movement Alignment.
Article in Cyborg and bionic systems (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Hybrid Convolutional, Mamba and Spiking Neural Network Architecture Search for Seizure Prediction.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Cytokines in age-related eye diseases: pathogenesis and potential targets for innovative therapies.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder.Scientific reports · 2025Article
- Ensemble-based eye disease detection system utilizing fundus and vascular structures.Scientific reports · 2025Article
- EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection.PeerJ. Computer science · 2025Article
- Neuroanatomy-Informed Brain-Machine Hybrid Intelligence for Robust Acoustic Target Detection.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- A multi-domain graph convolutional network-based prediction model for personalized motor imagery action.Frontiers in neuroscience · 2025Article
- Neural Signature and Decoding of Unmanned Aerial Vehicle Operators in Emergency Scenarios Using Electroencephalography.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
The target detection based on electroencephalogram (EEG) signals is a new target detection method. This method recognizes the target by decoding the specific neural response when an operator observes the target, which has important theoretical and application values. This paper focuses on the EEG detection of low-quality video targets, which breaks through the limitation of previous target detection based on EEG signals only for high-quality video targets. We first design an experimental paradigm for EEG-based low-quality video target detection and propose an epoch extraction method based on eye movement signals to solve the asynchronous problem faced by low-quality video target detection. Then, the neural representation in the process of operator recognition is analyzed based on the time domain, frequency domain, and source space domain, respectively. We design the time-frequency features based on continuous wavelet transform according to the neural representation and obtain an average decoding test accuracy of 84.56%. The research results of this paper lay the foundation for the development of a video target detection system based on EEG signals in the future.
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Registered trials
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