Investigating Brain Connectivity with Graph Neural Networks and GNNExplainer

Zhdanov, Maksim; Steinmann, Saskia; Hoffmann, Nico

doi:10.1109/icpr56361.2022.9956201

Cited by 7 publications

(7 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite most of the surveyed papers being relatively recent, a wide range of GNN-based methods has already been proposed to classify EEG signals in a diverse set of tasks, such as emotion recognition, brain-computer interfaces, and psychological and neurodegenerative disorders and diseases [46], [53], [54], [56], [58], [61], [70], [72], [75], [83], [89], [106] Chebyshev Graph Convolution ✗ ✓ ✗ [49], [51], [55], [57], [59], [66], [67], [69], [71], [74], [76]- [78], [80], [82], [85], [90], [97], [99], [104] Graph Attention Network ✓ ✗ ✗ [60], [62], [73], [84], [88], [94], [98] This survey categorises the proposed GNN models in terms of their inputs and modules. Specifically, these are brain graph structure, node features and their preprocessing, GCN layers, node pooling mechanisms, and formation of graph embeddings.…”

Section: Discussionmentioning

confidence: 99%

“…It is worth noting that this paper does not follow a systematic review methodology; therefore, we do not assert that our findings are exhaustive. Instead, our objective is to offer [54], [62], [76], [106] Concatenate node embeddings ✗ [47], [55]- [60], [64], [66], [67], [70], [74], [77], [80], [81], [86], [87],…”

Section: Limitations Of Our Surveymentioning

confidence: 99%

“…Instead, we suggest a comparative experimental study to be better suited to explore the various explainability options of GNN explainability. However, to maintain the comprehensiveness of this survey, we list the papers that report the use of certain methods of model explainability: [24], [50], [55], [89], [105], [106].…”

Section: Limitations Of Our Surveymentioning

confidence: 99%

See 2 more Smart Citations

Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Limitations Of Our Surveymentioning

confidence: 99%

See 1 more Smart Citation

Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…In other studies, each brain region or electrode not only exhibits spatial correlation but also generates temporal signals, such as an EEG and fMRI. To capture this temporal dynamic information, researchers have introduced the spatial-temporal GNN [82,83]. Furthermore, various scales and distinct graph construction methods offer different perspectives for expressing graph information.…”

Section: Ginmentioning

confidence: 99%

“…Subsequently, a CNN was employed to capture the temporal relationships between adjacent segments. Zhdanov et al [82] used a CNN to extract EEG temporal features, followed by the utilization of a high-order GNN [114] to extract spatial features. Shan et al [66] introduced a spatial-temporal GNN model, where each spatial-temporal block comprised two temporal convolution layers and one spatial convolution layer.…”

Section: Cnn-basedmentioning

confidence: 99%

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

Zhang,

Yang,

Zhang

et al. 2023

Brain Sciences

View full text Add to dashboard Cite

Neurological disorders (NDs), such as Alzheimer’s disease, have been a threat to human health all over the world. It is of great importance to diagnose ND through combining artificial intelligence technology and brain imaging. A graph neural network (GNN) can model and analyze the brain, imaging from morphology, anatomical structure, function features, and other aspects, thus becoming one of the best deep learning models in the diagnosis of ND. Some researchers have investigated the application of GNN in the medical field, but the scope is broad, and its application to NDs is less frequent and not detailed enough. This review focuses on the research progress of GNNs in the diagnosis of ND. Firstly, we systematically investigated the GNN framework of ND, including graph construction, graph convolution, graph pooling, and graph prediction. Secondly, we investigated common NDs using the GNN diagnostic model in terms of data modality, number of subjects, and diagnostic accuracy. Thirdly, we discussed some research challenges and future research directions. The results of this review may be a valuable contribution to the ongoing intersection of artificial intelligence technology and brain imaging.

show abstract