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graph convolution network-based eeg signal analysis: a review

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2025年 1-17页

作者： Xiong, Hui Yan, Yan Chen, Yimei Liu, Jinzhen Tiangong Univ Sch Control Sci & Engn Tianjin 300387 Peoples R China TianGong Univ Key Lab Intelligent Control Elect Equipment Tianjin 300387 Peoples R China Tiangong Univ Sch Artificial Intelligence Tianjin 300387 Peoples R China

With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of graph convolution network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN. An exhaustive classification of methods and a systematic analysis of key modules, such as brain map construction, node feature extraction, and GCN architecture design, are presented. In addition, we pay special attention to several key research issues related to GCN. This review enhances the understanding of the future potential of GCN in the field of EEG signal analysis. At the same time, several valuable development directions are sorted out for researchers in related fields, such as analysing the applicability of different GCN layers, building task-oriented GCN models, and improving adaptation to limited data.

关键词： graph convolution network Electroencephalography Deep learning graph neural network Brain-computer interface

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graph convolution network for fraud detection in bitcoin transactions

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SCIENTIFIC REPORTS 2025年第1期15卷 1-14页

作者： Asiri, Ahmad Somasundaram, K. King Khalid Univ Appl Coll Mahail Aseer Dept Math Abha Saudi Arabia Amrita Vishwa Vidyapeetham Amrita Sch Phys Sci Coimbatore Dept Math Coimbatore India

Anti-money laundering has been an issue in our society from the beginning of time. It simply refers to certain regulations and laws set by the government to uncover illegal money, which is passed as legal income. Now, with the emergence of cryptocurrency, it ensures pseudonymity for users. Cryptocurrency is a type of currency that is not authorized by the government and does not exist physically but only on paper. This provides a better platform for criminals for their illicit transactions. New algorithms have been proposed to detect illicit transactions. Machine learning and deep learning algorithms give us hope in identifying these anomalies in transactions. We have selected the Elliptic Bitcoin Dataset. This data set is a graph data set generated from an anonymous blockchain. Each transaction is mapped to real entities with two categories: licit and illicit. Some of them are not labeled. We have run different algorithms for predicting illicit transactions like Logistic Regression, Long Short Term Memory, Support Vector Machine, Random Forest, and a variation of graph Neural networks, which is called graph convolution network (GCN). GCN is of special interest in our case. Different evaluation parameters such as accuracy, ROC and F1 score are analyzed for different models. Our experimental results show that the proposed GCN model gives the accuracy \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$98.5\%$$\end{document}, the AUC 0.9444 and the RMSE 0.1123, which concludes that our GCN is better than the existing models, in particular with the model proposed in Weber et al. (Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics, 2019. http://***/abs/1908.02591).

关键词： BITCOIN Cryptocurrency Machine learning Deep learning graph convolution network

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Spatial temporal graph convolution network for the analysis of regional wall motion in left ventricular opacification echocardiography

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 103卷

作者： Cui, Rongpu He, Wenfeng Huang, Junhao Zhang, Junyan Zhang, Haozhe Liang, Shichu He, Yujun Liu, Zhiyue Gao, Shaobing He, Yong Peng, Jian Huang, He Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ West China Hosp Dept Cardiol Chengdu 610041 Peoples R China

The analysis of regional wall motion (RWM) in echocardiography is critical for the diagnosis of coronary heart disease (CHD). Traditional detections of regional wall motion abnormalities (RWMAs) mainly rely on manual observation and experience. Despite the rapid development of deep learning analysis of medical data, current automated analyses of myocardial function are mainly quantitative measurements, limited by the accuracy of left ventricular (LV) segmentation results. We expect to develop a qualitative deep learning method, not relying excessively on quantitative measurements, to detect RWMAs and determine the infarct coronary artery, in left ventricular opacification (LVO). The architecture we proposed constructs the LV wall segments into graphs, using spatial temporal graph convolution network (ST-GCN) and Multi-head Self-attention (MSA) to extract lesion features. We adopted modified dynamic and channel-non-shared topological strategy, with specific structural topology for RWM. The three ST-GCN classifiers are used to detect RWMAs related to the three coronary artery branches respectively, and summarize results. In our experiments, using the data we collected in the clinic, we trained and evaluated three ST-GCNs with different data sets, achieving the best performance compared with several state-of-the-art models. The comprehensive accuracy, sensitivity and specificity are 81.83%, 80% and 83.67% respectively. The experimental results demonstrate the feasibility of GCN analysis for the RWM in LVO.

关键词： Coronary heart disease Regional wall motion Echocardiography Deep learning graph convolution network Multi-head self-attention

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Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by graph convolution network

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JOURNAL OF MARINE SCIENCE AND ENGINEERING 2025年第3期13卷 439-439页

作者： Feng, Wu Geng, Xiulin He, Xiaoyu Hu, Miao Luo, Jie Bi, Meihua Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Sci Tech Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China

Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which is crucial for monitoring. Deep learning has been explored for sea ice extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from remote sensing images. A novel model named GEFU-Net, a modification of U-Net, is presented. The self-established graph reconstruction module is employed to convert features into graph data and construct the adjacency matrix using a global adaptive average similarity threshold. graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. Experimental results using the sea ice dataset of the Ross Sea in the Antarctic, produced by Sentinel-2, demonstrate that our GEFU-Net achieves the best performance compared to other commonly used segmentation models. Specifically, it achieves an accuracy of 97.52%, an Intersection over Union of 95.66%, and an F1-Score of 97.78%. Additionally, fewer model parameters and good inference speed are demonstrated, indicating strong potential for practical ice mapping applications.

关键词： sea ice extraction true-color imagery sentinel-2 image deep learning feature enhancement graph convolution network

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Dynamic Spatial-Temporal graph convolution network for E-Bike Traffic Flow Forecasting

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 5453-5466页

作者： Yu, Dongjin Guo, Gangming Wang, Dongjing Ouyang, Tianpei Wan, Feng Liu, Jitao Xu, Guandong Deng, Shuiguang Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China Yunnan Univ Finance & Econ Yunnan Key Lab Serv Comp Kunming 650221 Peoples R China Educ Univ Hong Kong Hong Kong Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

Electric bicycles (e-bikes) are playing increasingly important roles in satisfying people's personal mobility and short trips in an eco-friendly (reducing carbon dioxide emission) and efficient way. However, the popularity of e-bikes brings new challenges to public transport management. Fine-grained and accurate forecasting of traffic flow can help provide scientific decision support for urban planners, thereby alleviating many urban management problems, such as traffic congestion, parking lots and charging stations planning, etc. However, traffic flow forecasting is a highly non-linear problem with complex spatial and temporal dependencies. In particular, people usually have more flexible and random mobility when riding e-bikes. Existing methods do not effectively utilize the trajectory data of vehicles or capture the dynamic changes of spatial-temporal dependencies. In this paper, we propose a Dynamic Spatial-Temporal graph convolution network (DSTGCN) for e-bike traffic flow forecasting. Specifically, we have designed an inter-road transition flow embedding module to extract spatial attention matrix between roads, in response to the flexible mobility of e-bikes. To cope with the dense urban road network, we combine graph convolution with the spatial attention matrix, making the model more efficient and refined in capturing spatial-related information. Then, a channel attention component is designed to enhance the ability of the graph convolution module to model the dependencies between channels explicitly. Finally, we conducted extensive experiments on both the real-world e-bike dataset and simulation scenario dataset, demonstrating that DSTGCN outperforms the baselines, including several state-of-the-art methods.

关键词： Traffic flow forecasting graph convolution network intelligent transportation system intelligent transportation system trajectory trajectory trajectory

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IGGCN: Individual-guided graph convolution network for pedestrian trajectory prediction

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DIGITAL SIGNAL PROCESSING 2025年 156卷

作者： Chen, Wangxing Sang, Haifeng Wang, Jinyu Zhao, Zishan Shenyang Univ Technol Sch Informat Sci & Engn Shenliao West Rd Shenyang 110870 Liaoning Peoples R China

Accurately predicting the future trajectory of pedestrians is crucial for applications such as autonomous driving and robot navigation. graph convolution is widely used in trajectory prediction tasks due to its scalability and adaptive feature-learning capabilities. However, there are two problems with pedestrian trajectory prediction methods based on graph convolution: 1. Previous methods struggled to adjust social interactions according to the attributes of different pedestrians, making it difficult to accurately model the relative importance between different pedestrians and others;2. Previous methods lacked dynamic processing of pedestrian spatial-temporal interaction features to capture high-level spatial-temporal interaction features effectively. Therefore, we propose an Individual-Guided graph convolution network (IGGCN) for pedestrian trajectory prediction. To tackle problem 1, we design an individual-guided interaction module that can adjust pedestrian social interaction modeling according to the pedestrian's attributes, thereby achieving an accurate description of the relative importance of pedestrians. We extend the module to temporal interaction modeling to further achieve an accurate description of the relative importance of time frames. To address problem 2, we design a deformable convolution module to dynamically process spatial-temporal interaction features through deformable convolution kernels, facilitating the capture of high-level spatial-temporal interaction features. We evaluate our method on the ETH, UCY, and SDD datasets. Quantitative analysis shows that our method has lower prediction errors than the current state-of-the-art methods. Qualitative analysis further reveals that our method effectively eliminates the influence of irrelevant pedestrians and accurately models the spatial-temporal interaction relationship of pedestrians.

关键词： Pedestrian trajectory prediction graph convolution network Individual-guided interaction Deformable convolution

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Dual-level constraint based distributed graph convolution network for multimodal emotion recognition in conversation

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NEUROCOMPUTING 2025年 618卷

作者： Xiang, Yan Wang, Lu Tan, Xiaocong Guo, Junjun Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Peoples R China

Multimodal emotion recognition in conversation (MERC) has attracted increasing attention for its wide applications. While previous methods have focused on extracting and merging features from various modalities, they often do not sufficiently consider interactions within and across modalities, as well as the semantic and modal correlations of utterances within a dialogue. To address this gap, we propose a dual-level constraint-based distributed GCN model. This model constructs a graph convolution network by simultaneously incorporating multiple modal-central graphs to capture the multimodal features of utterances. Additionally, we adopt a dual-level constraint strategy to identify the semantic and modal correlation of each speaker's utterances in a dialogue. By comprehensively capturing the utterance features, our model significantly enhances emotion classification performances. The comparative experiment results show that our model outperforms the state-of-the-art models on the benchmark datasets IEMOCAP, MELD and M3ED. In addition, we verify the effectiveness of each component of our model by ablation experiments.

关键词： Multimodal emotion recognition in conversation graph convolution network Sentiment analysis Multimodal interaction Dual-level constraint strategy

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Cross-resolution person re-identification based on double-layer graph convolution network

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 147卷

作者： Zhu, Fuzhen Li, Donglin Sun, Ce Zhu, Bing Heilongjiang Univ Coll Elect Engn Harbin 150080 Peoples R China Army Mil Transportat Univ Dept Mil Vehicle Engn Tianjin 300161 Peoples R China Harbin Inst Technol Inst Image Informat Technol & Engn Harbin 150001 Peoples R China

Cross-resolution person re-identification aims to search for specific person under different camera views and different locations, which has been widely used in the field of intelligent security. To get higher accuracy of person re-identification and reduce the interference of complex background on person re-identification, a crossresolution person re-identification based on double-layer graph convolution network was proposed in this paper. It mainly includes super-resolution reconstruction (SRR) network and person re-identification network. In the super-resolution network, we introduced a new SRR network combined Transformer (TNSR) with meta-learning up-sampling module, which can ensure the network focuses on the key areas of the image and gets higher resolution SRR images easier for identification. In the person re-identification network, the human key point detection model was introduced, which can effectively reduce the interference of complex background and extract the key point features of the human body. Then we used these key point features as the nodes of the graph convolution and formed the topological map of the human body. Finally, the proposed feature fusion method based on the double-layer graph convolution network was used to gather the information between adjacent nodes and fuse them into a new feature representation. Experimental results on commonly used person reidentification datasets of Concurrent Actions in Video Analysis and Recognition (CAVIAR), Modified LowResolution-Market1501(MLR-Market1501) and Modified Low-Resolution-The Chinese University of Hong Kong 03(MLR-CUHK03) showed that, compared with the state of the art person re-identification method, our method improved the Rank-1, Rank-5 and Rank-10 by 2.67%, 2.23% and 0.7% respectively.

关键词： Person re-identification Cross-resolution person re-identification graph convolution network Transformer Intelligent security

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Multi-source information fusion for dynamic safety risk prediction of aerial building machine using spatial-temporal multi-graph convolution network

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ADVANCED ENGINEERING INFORMATICS 2025年 65卷

作者： Wang, Jiaqi Fan, Yuqing Pan, Xi Sun, Jun Zhang, Limao Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China China Nucl Power Engn Co Ltd 117 West Third Ring North Rd Beijing 100048 Peoples R China Shanghai Construct Grp Co Ltd Gen Engn Inst Shanghai Construct Grp 666 Dongdaming Rd Shanghai 200080 Peoples R China

Aerial Building Machine (ABM) is an innovative and comprehensive construction equipment employed in highrise building construction, capable of climbing upwards through the use of multiple hydraulic cylinders. The lifting operation of ABM is a critical phase for construction safety, yet there is limited research on forecasting and warning for the ABM lifting process. This study proposes a novel spatial-temporal forecasting model that combines the graph Neural network (GNN) and Temporal convolutional network (TCN), along with a computational framework for modeling multi-source data fusion and multi-graph construction. Specifically, fastDTW and direct-LiNGAM are used to capture and analyze the internal relationships within monitoring data from diverse sensors. The proposed Multi-graph TCN (MGTCN) fuses spatial and temporal features to make multi-step ahead predictions and provides explanations for dynamic safety risks. An ABM case in China is employed to illustrate the feasibility and effectiveness of the proposed framework. The results indicate that: (1) MGTCN exhibits high accuracy in multi-step prediction, with an average R2 of 0.917 at 10-step prediction;(2) MGTCN demonstrates strong robustness, maintaining the R2 change rate within 3% for every two steps up to the 20-step prediction;(3) The proposed model outperforms others in terms of generalization and stability when compared to models using single-source data or single-graph construction. The contribution of this research lies in transforming large-scale engineering data into graph-based prior knowledge, which is input into a spatial-temporal fusion GNN prediction model, improving accuracy and robustness. The significance of this study is in addressing a gap in ABM risk prediction and advancing time series prediction research in construction engineering. The utilization of intelligent prediction techniques not only ensures the safety management of lifting operations with ABM but also promises to improve the inf

关键词： Aerial building machine Multi-source information fusion graph convolution network Temporal convolution network Multi-step ahead prediction

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graph convolution network based State Space Model for Wireless Traffic Prediction 25

Graph Convolution Network based State Space Model for Wirele...

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IEEE Wireless Communications and networking Conference (IEEE WCNC)

作者： Zhou, Hao Yao, Dunyuan Chen, Binbin Yu, Ke Wu, Xiaofei Beijing Univ Posts & Telecommun Beijing Peoples R China Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9798350303582;9798350303599

With the rapid advancement of communication networks, wireless traffic prediction plays a pivotal role in resource allocation and energy management. However, due to the complex spatio-temporal characteristics inherent in real-world traffic data, capturing its intrinsic features accurately has proven to be challenging, leading to unsatisfactory prediction accuracy. In this paper, we propose a graph convolution network based State Space Model (GSSM) aimed at predicting call detail records wireless traffic. We utilize both fixed and adaptive weighted matrices to learn the complex spatial dependencies among base stations. The graph convolution network is conducted to accurately estimate the parameters of the state space model. Additionally, we employ a mixture Gaussian hypothesis to provide more flexibility for the state space model when predicting traffic data. Our experimental results demonstrate that the proposed method outperforms the baseline methods. Furthermore, we conduct extensive visualization, ablation and parameter analysis experiments to confirm the effectiveness of our proposed approach.

关键词： Traffic prediction state space model graph convolution network mixture Gaussian model

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