A Novel Complex Network-Based Graph Convolutional Network in Major Depressive Disorder Detection

Sun, Xinlin; Ma, Chao; Chen, Peiyin; Li, Mengyu; Wang, He; Dang, Weidong; Mu, Chaoxu; Gao, Zhong-Ke

doi:10.1109/tim.2022.3211559

Cited by 12 publications

(5 citation statements)

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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%

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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%

“…An example of such a model utilises twobranch GNN to learn from both FC-and SC-based brain graph structure [63]. Alternatively, the individual frequency bands of EEG signals can be used to construct various graph views [85]. In some instances, reducing the number of nodes in the graph might be desirable.…”

Section: Type Of Graph Convolutional Layermentioning

confidence: 99%

Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…As evident in Table I stages. The realm of GCNs in EEG, particularly within the context of dementia-related disorders, is experiencing rapid growth [1], [37], [38], [45], [46]. By leading the use of GCN-net for EEG-based differential identification of dementia-related disorders, this study not only pioneers a novel approach but also surpasses existing thresholding frameworks, demonstrating superior performance with an accuracy and F1-score of 76.35% and 0.73, respectively, in the broad band (see Table II).…”

Section: As Indicated In Tablementioning

confidence: 99%

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset

Adebisi,

Lee,

Veluvolu

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Diagnosing and treating dementia, including mild 1 cognitive impairment (MCI), is challenging due to diverse disease 2 types and overlapping symptoms. Early MCI detection is vital 3 as it can precede dementia, yet distinguishing it from later 4 stage dementia is intricate due to subtle symptoms. The primary 5 objective of this study is to adopt a complex network perspective 6 to unravel the underlying pathophysiological mechanisms of 7 dementia-related disorders. Leveraging the extensive availability 8 of electroencephalogram (EEG) data, our study focuses on 9 the meticulous identification and analysis of EEG-based brain 10 functional network (BFNs) associated with dementia-related dis-11 orders. To achieve this, we employ the Phase Lag Index (PLI) 12 as a connectivity measure, offering a comprehensive view of 13 neural interactions. To enhance the analytical rigor, we introduce 14 a data-driven threshold selection technique. This innovative 15 approach allows us to compare the topological structures of the 16 formulated BFNs using complex network measures quantitatively 17 and statistically. Furthermore, we harness the power of these 18 BFNs by utilizing them as pre-defined graph inputs for a Graph 19 Convolution Network (GCN-net) based approach. The results 20 demonstrate that graph theory metrics, such as the rich-club 21 coefficient, transitivity, and assortativity coefficients, effectively 22 distinguish between MCI, Alzheimer's disease (AD) and vascular 23 dementia (VD). Furthermore, GCN-net achieves high accuracy 24 (95.07% delta, 80.62% theta) and F1-scores (0.92 delta, 0.67 25 theta), highlighting the effectiveness of EEG-based BFNs in the 26 analysis of dementia-related disorders.27

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“…Nevertheless, traditional analysis and classification methods based on EEG features often overlook the interrelationships between electrodes, treating them as isolated nodes. To analyze the abnormal topological architecture in the brain between MDD and NC, researchers have proposed brain functional networks based on functional connection metrics, such as weighted phase lag index (wPLI), phase locking value (PLV), Pearson correlation coefficient (Corr), complete graph (CG), and others [12,23,24]. In an effort to minimize redundancy and identify the most discriminative EEG features for MDD, Chang et al introduced a stochastic search algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals

Wang,

Peng,

Han

et al. 2024

J. Neural Eng.

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Objective. Identifying major depressive disorder (MDD) using objective physiological signals has become a pressing challenge. Approach. Hence, this paper proposes a graph convolutional transformer network (GCTNet) for accurate and reliable MDD detection using electroencephalogram (EEG) signals. The developed framework integrates a residual graph convolutional network (ResGCN) block to capture spatial information and a Transformer block to extract global temporal dynamics. Additionally, we introduce the contrastive cross-entropy (CCE) loss that combines contrastive learning to enhance the stability and discriminability of the extracted features, thereby improving classification performance. Main results. The effectiveness of the GCTNet model and CCE loss was assessed using EEG data from 41 MDD patients and 44 normal controls (NC), in addition to a publicly available dataset. Utilizing a subject-independent data partitioning method and 10-fold cross-validation, the proposed method demonstrated significant performance, achieving an average Area Under the Curve (AUC) of 0.7693 and 0.9755 across both datasets, respectively. Comparative analyses demonstrated the superiority of the GCTNet framework with CCE loss over state-of-the-art algorithms in MDD detection tasks. Significance. The proposed method offers an objective and effective approach to MDD detection, providing valuable support for clinical-assisted diagnosis.

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A Novel Complex Network-Based Graph Convolutional Network in Major Depressive Disorder Detection

Cited by 12 publications

References 50 publications

Graph Neural Network-Based EEG Classification: A Survey

Graph Neural Network-Based EEG Classification: A Survey

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals

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