ArticleBrain sciences2022
Applying Multiple Functional Connectivity Features in GCN for EEG-Based Human Identification.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain-Computer Interface for EEG-Based Authentication: Advancements and Practical Implications.Sensors (Basel, Switzerland) · 2025Pooled it
- LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.Brain sciences · 2025Article
- Adaptive GCN and Bi-GRU-Based Dual Branch for Motor Imagery EEG Decoding.Sensors (Basel, Switzerland) · 2025Article
- Threats and Mitigation Strategies for Electroencephalography-Based Person Authentication.International journal of telemedicine and applications · 2025Review
- The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks.Human brain mapping · 2024Article
- TSANN-TG: Temporal-Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition.Brain sciences · 2024Article
- Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition.Brain sciences · 2022Article
- Towards precision EEG connectomics: Evaluating the benefits of dense sampling.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
EEG-based human identification has gained a wide range of attention due to the further increase in demand for security. How to improve the accuracy of the human identification system is an issue worthy of attention. Using more features in the human identification system is a potential solution. However, too many features may cause overfitting, resulting in the decline of system accuracy. In this work, the graph convolutional neural network (GCN) was adopted for classification. Multiple features were combined and utilized as the structure matrix of the GCN. Because of the constant signal matrix, the training parameters would not increase as the structure matrix grows. We evaluated the classification accuracy on a classic public dataset. The results showed that utilizing multiple features of functional connectivity (FC) can improve the accuracy of the identity authentication system, the best results of which are at 98.56%. In addition, our methods showed less sensitivity to channel reduction. The method proposed in this paper combines different FCs and reaches high classification accuracy for unpreprocessed data, which inspires reducing the system cost in the actual human identification system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.