IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第9页

Self-Clustering Hierarchical Multi-Agent Reinforcement Learning With Extensible Cooperation Graph

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-04 DOI: 10.1109/TETCI.2024.3449873

Qingxu Fu;Tenghai Qiu;Jianqiang Yi;Zhiqiang Pu;Xiaolin Ai

{"title":"Self-Clustering Hierarchical Multi-Agent Reinforcement Learning With Extensible Cooperation Graph","authors":"Qingxu Fu;Tenghai Qiu;Jianqiang Yi;Zhiqiang Pu;Xiaolin Ai","doi":"10.1109/TETCI.2024.3449873","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449873","url":null,"abstract":"Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. However, classic non-hierarchical MARL algorithms still cannot address various complex multi-agent problems that require hierarchical cooperative behaviors. The cooperative knowledge and policies learned in non-hierarchical algorithms are implicit and not interpretable, thereby restricting the integration of existing knowledge. This paper proposes a novel hierarchical MARL model called Hierarchical Cooperation Graph Learning (HCGL) for solving general multi-agent problems. HCGL has three components: a dynamic Extensible Cooperation Graph (ECG) for achieving self-clustering cooperation; a group of graph operators for adjusting the topology of ECG; and an MARL optimizer for training these graph operators. HCGL's key distinction from other MARL models is that the behaviors of agents are guided by the topology of ECG instead of policy neural networks. ECG is a three-layer graph consisting of an agent node layer, a cluster node layer, and a target node layer. To manipulate the ECG topology in response to changing environmental conditions, four graph operators are trained to adjust the edge connections of ECG dynamically. The hierarchical feature of ECG provides a unique approach to merge primitive actions (actions executed by the agents) and cooperative actions (actions executed by the clusters) into a unified action space, allowing us to integrate fundamental cooperative knowledge into an extensible interface. In our experiments, the HCGL model has shown outstanding performance in multi-agent benchmarks with sparse rewards. We also verify that HCGL can easily be transferred to large-scale scenarios with high zero-shot transfer success rates.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1688-1698"},"PeriodicalIF":5.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Cooperative Control of Multi-Agent Systems Through Event-Triggered Model-Free Reinforcement Learning

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-04 DOI: 10.1109/TETCI.2024.3451484

Chaoxu Mu;Zhuo Tang;Ke Wang

{"title":"Optimal Cooperative Control of Multi-Agent Systems Through Event-Triggered Model-Free Reinforcement Learning","authors":"Chaoxu Mu;Zhuo Tang;Ke Wang","doi":"10.1109/TETCI.2024.3451484","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3451484","url":null,"abstract":"This paper addresses the optimal cooperative control problem for nonlinear multi-agent systems with completely unknown dynamics and proposes a learning control scheme based on the event-triggered mechanisms. The problem is reformulated as a multi-agent differential graphical game, and an off-policy integral reinforcement learning algorithm is introduced by deriving off-policy Bellman equations. To reduce the computational burden of the controller, an event-triggered mechanism is integrated into the adaptive learning process. To overcome the limitations of static triggering, the dynamic variable is introduced to utilize past triggering information. The theoretical proof demonstrates the asymptotic stability of the system and a numerical example validates the effectiveness of the proposed control scheme. Finally, in the case of the multiple manipulator system, a comparison of four control schemes shows that the proposed method not only ensures the system's control performance but also achieves a larger triggering interval, reducing the update frequency of the controller and saving communication bandwidth.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1699-1711"},"PeriodicalIF":5.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MASER: Multi-Order Attention and Semantic-Enhanced Representation Model for Complex Text Recommendation

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-04 DOI: 10.1109/TETCI.2024.3442872

Pei-Yuan Lai;Qing-Yun Dai;De-Zhang Liao;Zhe-Rui Yang;Xiao-Dong Liao;Chang-Dong Wang;Min Chen

{"title":"MASER: Multi-Order Attention and Semantic-Enhanced Representation Model for Complex Text Recommendation","authors":"Pei-Yuan Lai;Qing-Yun Dai;De-Zhang Liao;Zhe-Rui Yang;Xiao-Dong Liao;Chang-Dong Wang;Min Chen","doi":"10.1109/TETCI.2024.3442872","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3442872","url":null,"abstract":"In some recommendation platforms, the recommended items are composed of the complex text, and the target users are also described by the complex text. These texts are usually long, highly specialized, logically structured, and have significant differences, such as recommending technical demands of enterprises to technology researchers. Although some recommendation methods based on text representation can be used to solve this problem, such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM), they may encounter challenges from different perspectives, e.g., path connectivity of representations, and the relationship between representations and recommended items. The complex text recommendation is an important problem that remains largely unsolved. In order to overcome the aforementioned challenges, by taking the technology commercialization as an example, which aims to recommend demands to researchers, we propose a novel complex text recommendation model called <bold>Multi-order <bold>Attention and <bold>Semantic <bold>Enhanced <bold>Representation (MASER). By integrating additional information into text vector representationsuch as structural relationship information for extended keywords, and semantic information for entity description texts the proposed model enhances complex text recommendation effectiveness significantly. Extensive experiments have been conducted on real datasets, confirming the advantages of the MASER model and the attention mechanism's effectiveness on complex text recommendation.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1743-1755"},"PeriodicalIF":5.3,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ARC: A Layer Replacement Compression Method Based on Fine-Grained Self-Attention Distillation for Compressing Pre-Trained Language Models

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-03 DOI: 10.1109/TETCI.2024.3418837

Daohan Yu;Liqing Qiu

{"title":"ARC: A Layer Replacement Compression Method Based on Fine-Grained Self-Attention Distillation for Compressing Pre-Trained Language Models","authors":"Daohan Yu;Liqing Qiu","doi":"10.1109/TETCI.2024.3418837","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3418837","url":null,"abstract":"The primary objective of model compression is to maintain the performance of the original model while reducing its size as much as possible. Knowledge distillation has become the mainstream method in the field of model compression due to its excellent performance. However, current knowledge distillation methods for medium and small pre-trained models struggle to effectively extract knowledge from large pre-trained models. Similarly, methods targeting large pre-trained models face challenges in compressing the model to a smaller scale. Therefore, this paper proposes a new model compression method called Attention-based Replacement Compression (ARC), which introduces layer random replacement based on fine-grained self-attention distillation. This method first obtains the important features of the original model through fine-grained self-attention distillation in the pre-training distillation stage. More information can be obtained by extracting the upper layers of the large teacher model. Then, the one-to-one Transformer-layer random replacement training fully explores the hidden knowledge of the large pre-trained model in the fine-tuning compression stage. Compared with other complex compression methods, ARC not only simplifies the training process of model compression but also enhances the applicability of the compressed model. This paper compares knowledge distillation methods for pre-trained models of different sizes on the GLUE benchmark. Experimental results demonstrate that the proposed method achieves significant improvements across different parameter scales, especially in terms of accuracy and inference speed.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"848-860"},"PeriodicalIF":5.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy Composite Learning Control of Uncertain Fractional-Order Nonlinear Systems Using Disturbance Observer

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-03 DOI: 10.1109/TETCI.2024.3449890

Zhiye Bai;Shenggang Li;Heng Liu

{"title":"Fuzzy Composite Learning Control of Uncertain Fractional-Order Nonlinear Systems Using Disturbance Observer","authors":"Zhiye Bai;Shenggang Li;Heng Liu","doi":"10.1109/TETCI.2024.3449890","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449890","url":null,"abstract":"Noting that in traditional adaptive fuzzy controller (AFC) design, only the convergence of tracking error rather than fuzzy approximation error can be guaranteed. This paper focuses on tracking control of fractional-order systems subjected to model uncertainties together with external disturbances. Firstly, an AFC that blends the fuzzy logic system and the input constraint is proposed, where a disturbance observer is constructed to estimate the compounded disturbance. To improve the fuzzy approximation performance, a fractional-order serial parallel estimation model that combines with a fuzzy logic system and a disturbance observer is exploited to generate prediction errors, and both tracking errors and prediction errors are utilized simultaneously to construct parameter update laws, so that a composite learning fuzzy controller (CLFC) is implemented. In addition, a compound disturbance observer is proposed based on the system state and the prediction error while the disturbance estimation error is ensured to remain inside a bounded closed set. The proposed CLFC can not only assure the stability of the closed-loop system but also achieve an accurate estimation of function uncertainties and unknown compounded disturbances. Finally, the effectiveness of the proposed control algorithm is demonstrated via simulation results.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"1078-1090"},"PeriodicalIF":5.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simultaneously Learning Deep Quaternion Reconstruction and Noise Convolutional Dictionary for Color Image Denoising

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-03 DOI: 10.1109/TETCI.2024.3449924

Zheng Zhou;Yongyong Chen;Yicong Zhou

{"title":"Simultaneously Learning Deep Quaternion Reconstruction and Noise Convolutional Dictionary for Color Image Denoising","authors":"Zheng Zhou;Yongyong Chen;Yicong Zhou","doi":"10.1109/TETCI.2024.3449924","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449924","url":null,"abstract":"Recently, many deep convolutional dictionary learning-based methods, integrating the traditional image representation methods with deep neural networks, have achieved great success in various image processing tasks. However, the existing approaches can be further improved with the following considerations: (1) They congenitally suffer from the high cross-channel correlation loss for color image processing tasks since they usually treat each color channel independently, not in a whole perspective. (2) They only build up a single reconstruction dictionary learning model to directly approximate images using several single dictionary atoms, which cannot make full use of the representative ability of the model. In this paper, we propose a simultaneously learning deep quaternion reconstruction and noise convolutional dictionary model. To fully explore the cross-channel correlation, we use the quaternion method to process the color image in a holistic way. An adaptive attentional weight of reconstruction and noise learning module is also developed for the optimal combination between reconstruction and noise learning. Experimental results for synthesis and real color image denoising have demonstrated the superiority of the proposed method over other state-of-the-art methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1766-1779"},"PeriodicalIF":5.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Q-Learning Based Model-Free $H_{infty }$ Control of Continuous-Time Nonlinear Systems: Theory and Application

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-02 DOI: 10.1109/TETCI.2024.3449870

Jun Zhao;Yongfeng Lv;Zhangu Wang;Ziliang Zhao

{"title":"Adaptive Q-Learning Based Model-Free $H_{infty }$ Control of Continuous-Time Nonlinear Systems: Theory and Application","authors":"Jun Zhao;Yongfeng Lv;Zhangu Wang;Ziliang Zhao","doi":"10.1109/TETCI.2024.3449870","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449870","url":null,"abstract":"Although model based <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control scheme for nonlinear continuous-time (CT) systems with unknown system dynamics has been extensively studied, model-free <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control of <italic>nonlinear CT systems via Q-learning is still a challenging problem. This paper develops a novel Q-learning based model-free <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control scheme for nonlinear CT systems, where the adaptive critic and actor continuously and simultaneously update each other, eliminating the need for iterative steps. As a result, a hybrid structure is avoided and there is no longer a requirement for an initial stabilizing control policy. To obtain the <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control of the CT nonlinear system, the Q-learning strategy is introduced to online resolve the <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control problem in a non-iterative approach, where the system dynamics are not required. In addition, a new learning law is further developed by utilizing a sliding mode scheme to online update the critic neural network (NN) weights. Due to the strong convergence of critic NN weights, the actor NN used in most <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> control algorithms is removed. Finally, numerical simulation and experimental results of an adaptive cruise control (ACC) system based on a real vehicle effectively demonstrate the feasibility of the presented control method and learning algorithm.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1143-1152"},"PeriodicalIF":5.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D-IMMC: Incomplete Multi-Modal 3D Shape Clustering via Cross Mapping and Dual Adaptive Fusion

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-02 DOI: 10.1109/TETCI.2024.3436866

Tianyi Qin;Bo Peng;Jianjun Lei;Jiahui Song;Liying Xu;Qingming Huang

{"title":"3D-IMMC: Incomplete Multi-Modal 3D Shape Clustering via Cross Mapping and Dual Adaptive Fusion","authors":"Tianyi Qin;Bo Peng;Jianjun Lei;Jiahui Song;Liying Xu;Qingming Huang","doi":"10.1109/TETCI.2024.3436866","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3436866","url":null,"abstract":"In recent years, with the rapid growth number of multi-modal 3D shapes, it has become increasingly important to efficiently recognize a vast number of unlabeled multi-modal 3D shapes through clustering. However, the multi-modal 3D shape instances are usually incomplete in practical applications, which poses a considerable challenge for multi-modal 3D shape clustering. To this end, this paper proposes an incomplete multi-modal 3D shape clustering method with cross mapping and dual adaptive fusion, termed as 3D-IMMC, to alleviate the negative impact of the missing modal instances in multi-modal 3D shapes, thus obtaining competitive clustering results. To the best of our knowledge, this paper is the first attempt to the incomplete multi-modal 3D shape clustering task. By exploring the spatial relationship between different 3D shape modalities, a spatial-aware representation cross-mapping module is proposed to generate representations of missing modal instances. Then, a dual adaptive representation fusion module is designed to obtain comprehensive 3D shape representations for clustering. Extensive experiments on the 3D shape benchmark datasets (i.e., ModelNet10 and ModelNet40) have demonstrated that the proposed 3D-IMMC achieves promising 3D shape clustering performance.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"99-108"},"PeriodicalIF":5.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End-to-End Clustering Enhanced Contrastive Learning for Radiology Reports Generation

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-02 DOI: 10.1109/TETCI.2024.3449876

Xinyao Liu;Junchang Xin;Qi Shen;Chuangang Li;Zhihong Huang;Zhiqiong Wang

{"title":"End-to-End Clustering Enhanced Contrastive Learning for Radiology Reports Generation","authors":"Xinyao Liu;Junchang Xin;Qi Shen;Chuangang Li;Zhihong Huang;Zhiqiong Wang","doi":"10.1109/TETCI.2024.3449876","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3449876","url":null,"abstract":"With the rapid growth of medical imaging data, radiologists must dedicate a significant amount of time to report writing. Automated generation of radiology reports not only alleviates the heavy workload of physicians but, more importantly, can reduce mistakes and oversights caused by insufficient experience. However, due to substantial data bias in medical data, prior studies using typical cross-entropy loss in encoder-decoder architectures often result in generalized descriptions of normal tissues and may overlook crucial clinical abnormalities. Therefore, we propose a clustering enhanced contrastive learning model named CECL to generate more diverse radiology reports, and it is worth noting that our method is end-to-end trainable. Specifically, an adaptive alignment fusion encoder-decoder network (AAF) is constructed by fusing the image features and text semantic features from the transformer decoder, eliminating information redundancy across different modalities. Moreover, a label-guided contrastive learning (LCL) module is proposed. In detail, clustering is performed on the fused features using Gaussian competition. Supervised contrastive learning is conducted based on the clustering results to enhance feature representation ability. We evaluate the CECL on two widely used publicly available datasets, IU X-ray and MIMIC-CXR, using NLG and CE metrics. The experimental results demonstrate that CECL can produce fluent reports with more descriptions of anomalies, outperforming other state-of-the-art methods with higher clinical correctness.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1780-1794"},"PeriodicalIF":5.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning EEG Motor Characteristics via Temporal-Spatial Representations

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-02 DOI: 10.1109/TETCI.2024.3425328

Tian-Yu Xiang;Xiao-Hu Zhou;Xiao-Liang Xie;Shi-Qi Liu;Hong-Jun Yang;Zhen-Qiu Feng;Mei-Jiang Gui;Hao Li;De-Xing Huang;Xiu-Ling Liu;Zeng-Guang Hou

{"title":"Learning EEG Motor Characteristics via Temporal-Spatial Representations","authors":"Tian-Yu Xiang;Xiao-Hu Zhou;Xiao-Liang Xie;Shi-Qi Liu;Hong-Jun Yang;Zhen-Qiu Feng;Mei-Jiang Gui;Hao Li;De-Xing Huang;Xiu-Ling Liu;Zeng-Guang Hou","doi":"10.1109/TETCI.2024.3425328","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3425328","url":null,"abstract":"Electroencephalogram (EEG) is a widely used neural imaging technique for modeling motor characteristics. However, current studies have primarily focused on temporal representations of EEG, with less emphasis on the spatial and functional connections among electrodes. This study introduces a novel two-stream model to analyze both temporal and spatial representations of EEG for learning motor characteristics. Temporal representations are extracted with a set of convolutional neural networks (CNN) treated as dynamic filters, while spatial representations are learned by graph neural networks (GNN) using learnable adjacency matrices. At each stage, a res-block is designed to integrate temporal and spatial representations, facilitating a fusion of temporal-spatial information. Finally, the summarized representations of both streams are fused with fully connected neural networks to learn motor characteristics. Experimental evaluations on the Physionet, OpenBMI, and BCI Competition IV Dataset 2a demonstrate the model's efficacy, achieving accuracies of <inline-formula><tex-math>$73.6%/70.4%$</tex-math></inline-formula> for four-class subject-dependent/independent paradigms, <inline-formula><tex-math>$84.2%/82.0%$</tex-math></inline-formula> for two-class subject-dependent/independent paradigms, and 78.5% for a four-class subject-dependent paradigm, respectively. The encouraged results underscore the model's potential in understanding EEG-based motor characteristics, paving the way for advanced brain-computer interface systems.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"933-945"},"PeriodicalIF":5.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0