Graph Neural Network with Multilevel Feature Fusion for EEG based Brain-Computer Interface

Kwak, Youngchul; Song, Woo‐Jin; Kim, Seong‐Eun

doi:10.1109/icce-asia49877.2020.9276983

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…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%

“…[48], [52], [56], [60], [62]- [64], [66], [67], [69], [72], [76], [77], [79], [80], [83], [84], [86], [91], [94], [100], [104] An alternative categorisation of the brain graph structures is the functional (FC) and the "structural" connectivity (SC). Generally, SC graphs are pre-defined, whereas FC graphs can be both pre-defined and learnable.…”

Section: Definition Of Brain Graph Structurementioning

confidence: 99%

“…A major limitation of most surveyed papers is the lack of generalisability to external datasets that might use a different number of EEG This is caused by (1) the use of ChebConv and (2) forming graph embedding by node feature concatenation [47], [55]- [60], [64], [66], [67], [70], [74], [77], [80], [81], [86], [87], [90]- [93], [98], [100]- [102], [104]. (1) can be addressed by utilising spatial GCN layers as suggested above, and (2) can be solved by using a readout function or a suitable node pooling mechanism, which coarsens the graph to a fixed number of nodes.…”

Section: A Limitations Of Surveyed Papersmentioning

confidence: 99%

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Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Definition Of Brain Graph Structurementioning

confidence: 99%

Section: A Limitations Of Surveyed Papersmentioning

confidence: 99%

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Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Yet spatial location and functional connectivity of EEG channels do not remain consistent [46]. Later studies have also used the functional connectivity between channels to construct adjacency matrices [20,47], or combined both spatial location information and functional connectivity relationships, using the adjacency matrix as a trainable parameter for better predictions [22]. However, these works assume static topological structures.…”

Section: B States Classification Using Two-stream Gcnmentioning

confidence: 99%

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

Chen

Xie

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring. Traditional anesthesia monitoring methods are not involved with the topological changes between electrodes covering the prefrontal-parietal cortices, by investigating electrocorticography (ECoG). To fill this gap, a framework based on the two-stream graph convolutional network (GCN) was proposed, i.e., one stream for extracting topological structure features, and the other one for extracting node features. The twostream graph convolutional network includes GCN Model 1 and GCN Model 2. For GCN Model 1, brain connectivity networks were constructed by using phase lag index (PLI), representing different structure features. A common adjacency matrix was founded through the dual-graph method, the structure features were expressed on nodes. Therefore, the traditional spectral graph convolutional network can be directly applied on the graphs with changing topological structures. On the other hand, the average of the absolute signal amplitudes was calculated as node features, then a fully connected matrix was constructed as the adjacency matrix of these node features, as the input of GCN Model 2. This method learns features of both topological structure and nodes of the graph, and uses a dual-graph approach to enhance the focus on topological structure features. Based on the ECoG signals of monkeys, results show that this method which can distinguish awake state, moderate sedation and deep sedation achieved an accuracy of 92.75% in group-level experiments and mean accuracy of 93.50% in subject-level experiments. Our work verifies the excellence of the graph convolutional network in anesthesia monitoring, the high recognition accuracy also shows that the brain network may carry neurological markers associated with anesthesia.

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“…Results on the PhysioNet dataset [132], [133] show that it is necessary to optimize the GNN structure. Kwak et al [134] improved this structure by introducing multilevel feature fusion to the GNN that alleviates the limitations of sequential convolutional and pooling layers, which results in each node losing their local information. In this model the feature representation is combined with the author's previous 3D-CNN [141] to improve the performance of brain motor imagery classification.…”

Section: B Electrical-based Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal,

Armin,

Denman

et al. 2021

Preprint

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Graph Neural Network with Multilevel Feature Fusion for EEG based Brain-Computer Interface

Cited by 6 publications

References 7 publications

Graph Neural Network-Based EEG Classification: A Survey

Graph Neural Network-Based EEG Classification: A Survey

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

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