Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review
Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused more on extracting EEG temporal features and considered the topology of EEG channels less due to the limitation...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10916656/ |
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| author | S. M. Atoar Rahman Md Ibrahim Khalil Hui Zhou Yu Guo Ziyun Ding Xin Gao Dingguo Zhang |
| author_facet | S. M. Atoar Rahman Md Ibrahim Khalil Hui Zhou Yu Guo Ziyun Ding Xin Gao Dingguo Zhang |
| author_sort | S. M. Atoar Rahman |
| collection | DOAJ |
| description | Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused more on extracting EEG temporal features and considered the topology of EEG channels less due to the limitation of rich spatial information. Graph neural networks (GNNs), as a new kind of deep learning method, can use EEG signals as graph vertices, capturing the hidden topological connections between signals. GNNs have made great progress in EEG studies due to the advantage. In this overview, we review the very new and fundamental models of GNNs and their modifications, such as graph regularized neural networks, graph convolutional neural networks, spatial-temporal graph neural networks, graph attention networks, and their variants in EEG signal analysis fields. The applications of GNNs are summarized in the domains of emotion detection, epilepsy seizure detection, stroke rehabilitation, Alzheimer’s disease diagnosis, motor imagery detection, neurological disease diagnosis, major depressive disorder, and driving fatigue detection. We employed a Systematic Literature Review (SLR) approach to select 79 papers for a comprehensive review. The current state is analyzed and forecasts are provided based on the available difficulties. We conclude by suggesting potential directions for future research in this rapidly developing topic. |
| format | Article |
| id | doaj-art-b2d5bc41a89147528ded3a8ec7dbd563 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b2d5bc41a89147528ded3a8ec7dbd5632025-08-20T03:40:52ZengIEEEIEEE Access2169-35362025-01-0113501675018710.1109/ACCESS.2025.354912010916656Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A ReviewS. M. Atoar Rahman0Md Ibrahim Khalil1Hui Zhou2https://orcid.org/0000-0002-4900-4294Yu Guo3Ziyun Ding4https://orcid.org/0000-0002-1400-792XXin Gao5https://orcid.org/0000-0003-0825-7487Dingguo Zhang6https://orcid.org/0000-0003-4803-7489School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, ChinaSchool of Engineering, University of Birmingham, Birmingham, U.K.Department of Electronic and Electrical Engineering, University of Bath, Bath, U.K.Department of Electronic and Electrical Engineering, University of Bath, Bath, U.K.Electroencephalography (EEG) can non-invasively measure neuronal events and reflect brain activity at different locations on the scalp. Early studies for EEG signal processing have focused more on extracting EEG temporal features and considered the topology of EEG channels less due to the limitation of rich spatial information. Graph neural networks (GNNs), as a new kind of deep learning method, can use EEG signals as graph vertices, capturing the hidden topological connections between signals. GNNs have made great progress in EEG studies due to the advantage. In this overview, we review the very new and fundamental models of GNNs and their modifications, such as graph regularized neural networks, graph convolutional neural networks, spatial-temporal graph neural networks, graph attention networks, and their variants in EEG signal analysis fields. The applications of GNNs are summarized in the domains of emotion detection, epilepsy seizure detection, stroke rehabilitation, Alzheimer’s disease diagnosis, motor imagery detection, neurological disease diagnosis, major depressive disorder, and driving fatigue detection. We employed a Systematic Literature Review (SLR) approach to select 79 papers for a comprehensive review. The current state is analyzed and forecasts are provided based on the available difficulties. We conclude by suggesting potential directions for future research in this rapidly developing topic.https://ieeexplore.ieee.org/document/10916656/Electroencephalogramdeep learninggraph neural networksvariants of graph neural networksapplications of graph neural network |
| spellingShingle | S. M. Atoar Rahman Md Ibrahim Khalil Hui Zhou Yu Guo Ziyun Ding Xin Gao Dingguo Zhang Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review IEEE Access Electroencephalogram deep learning graph neural networks variants of graph neural networks applications of graph neural network |
| title | Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review |
| title_full | Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review |
| title_fullStr | Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review |
| title_full_unstemmed | Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review |
| title_short | Advancement in Graph Neural Networks for EEG Signal Analysis and Application: A Review |
| title_sort | advancement in graph neural networks for eeg signal analysis and application a review |
| topic | Electroencephalogram deep learning graph neural networks variants of graph neural networks applications of graph neural network |
| url | https://ieeexplore.ieee.org/document/10916656/ |
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