ArticleScientific reports2022
Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 60 citations in OpenAlex.
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Seizure detection using hierarchical temporal trend integration and self-supervised learning.Scientific reports · 2026Article
- Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.Brain informatics · 2026Article
- Diffusion Model-Based Multi-Channel EEG Representation and Forecasting for Early Epileptic Seizure Warning.Interdisciplinary sciences, computational life sciences · 2026Article
- Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.Pediatric investigation · 2026Review
- Efficient and Accurate Epilepsy Seizure Prediction and Detection Based on Multi-Teacher Knowledge Distillation RGF-Model.Brain sciences · 2026Article
- Deep Learning Method Based on Multivariate Variational Mode Decomposition for Classification of Epileptic Signals.Brain sciences · 2025Article
- RDPNet: A Multi-Scale Residual Dilated Pyramid Network with Entropy-Based Feature Fusion for Epileptic EEG Classification.Entropy (Basel, Switzerland) · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Spatial Adaptive Selection using Binary Conditional Autoregressive Model with Application to Brain-Computer Interface.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Optimal graph representations and neural networks for multichannel time series data in seizure phase classification.Scientific reports · 2025Article
- Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network.Scientific reports · 2025Article
- Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Flexible Patched Brain Transformer model for EEG decoding.Scientific reports · 2025Article
- Leveraging interictal multimodal features and graph neural networks for automated planning of epilepsy surgery.Brain communications · 2025Article
- Unsupervised clustering of pre-ictal EEG in children: a reproducible and lightweight CPU-based workflow.Frontiers in neurology · 2025Article
- Editorial: Spatiotemporal & AI trends in neuroscience, neuroimaging, and neurooncology.Frontiers in neuroimaging · 2025Article
- Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions.Brain sciences · 2024Review
- Residual and bidirectional LSTM for epileptic seizure detection.Frontiers in computational neuroscience · 2024Article
- Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model.Imaging neuroscience (Cambridge, Mass.) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.
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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.