IEEE Transactions on Cognitive and Developmental Systems最新文献_第4页

An Impedance Recognition Framework Based on Electromyogram for Physical Human–Robot Interaction 基于肌电图的物理人机交互阻抗识别框架

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-08-13 DOI: 10.1109/TCDS.2024.3442172

Jing Luo;Chaoyi Zhang;Chao Zeng;Yiming Jiang;Chenguang Yang

{"title":"An Impedance Recognition Framework Based on Electromyogram for Physical Human–Robot Interaction","authors":"Jing Luo;Chaoyi Zhang;Chao Zeng;Yiming Jiang;Chenguang Yang","doi":"10.1109/TCDS.2024.3442172","DOIUrl":"10.1109/TCDS.2024.3442172","url":null,"abstract":"In physical human–robot interaction (pHRI), the interaction profiles, such as impedance and interaction force are greatly influenced by the operator's muscle activities, impedance and interaction force between the robot and the operator. Actually, parameters of interaction profiles are easy to be measured, such as position, velocity, acceleration, and muscle activities. However, the impedance cannot be directly measured. In some areas, it is difficult to capture the force information, especially where the force sensor is hard to be attached on the robots. In this sense, it is worth developing a feasible and simple solution to recognize the impedance parameters by exploring the potential relationship among the above mentioned interaction profiles. To this end, a framework of impedance recognition based on different time-based weight membership functions with broad learning system (TWMF-BLS) is developed for stable/unstable pHRI. Specifically, a linear weight membership function and a nonlinear weight membership function are proposed for stable and unstable pHRI by using the hybrid features for estimating the interaction force. And then the human arm impedance can be estimated without a biological model or a robot's model. Experimental results have demonstrated the feasibility and effectiveness of the proposed approach.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"205-218"},"PeriodicalIF":5.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227654","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

IEEE Transactions on Cognitive and Developmental Systems Information for Authors 电气和电子工程师学会《认知与发展系统》期刊为作者提供的信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-08-12 DOI: 10.1109/TCDS.2024.3436255

引用次数: 0

IEEE Transactions on Cognitive and Developmental Systems Publication Information 电气和电子工程师学会认知与发展系统论文集》出版信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-08-12 DOI: 10.1109/TCDS.2024.3436251

引用次数: 0

IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-08-12 DOI: 10.1109/TCDS.2024.3436253

引用次数: 0

Reinforcement-Learning-Based Multi-Unmanned Aerial Vehicle Optimal Control for Communication Services With Limited Endurance 基于强化学习的多无人机优化控制，用于续航时间有限的通信服务

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-08-12 DOI: 10.1109/TCDS.2024.3441865

Lu Dong;Pinle Ding;Xin Yuan;Andi Xu;Jie Gui

{"title":"Reinforcement-Learning-Based Multi-Unmanned Aerial Vehicle Optimal Control for Communication Services With Limited Endurance","authors":"Lu Dong;Pinle Ding;Xin Yuan;Andi Xu;Jie Gui","doi":"10.1109/TCDS.2024.3441865","DOIUrl":"10.1109/TCDS.2024.3441865","url":null,"abstract":"This article investigates the service path problem of multi-unmanned aerial vehicle (multi-UAV) providing communication services to multiuser in urban environments with limited endurance. Our goal is to learn an optimal multi-UAV centralized control policy that will enable UAVs to find the illumination areas in urban environments through curiosity-driven exploration and harvest energy to continue providing communication services to users. First, we propose a reinforcement learning (RL)-based multi-UAV centralized control strategy to maximize the accumulated communication service score. In the proposed framework, curiosity can act as an internal incentive signal, allowing UAVs to explore the environment without any prior knowledge. Second, a two-phase exploring protocol is proposed for practical implementation. Compared to the baseline method, our proposed method can achieve a significantly higher accumulated communication service score in the exploitation-intensive phase. The results demonstrate that the proposed method can obtain accurate service paths over the baseline method and handle the exploration-exploitation tradeoff well.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"219-231"},"PeriodicalIF":5.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227646","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

Channel-Selection-Based Temporal Convolutional Network for Patient-Specific Epileptic Seizure Detection 基于信道选择的时态卷积网络用于特定患者癫痫发作检测

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-25 DOI: 10.1109/TCDS.2024.3433551

Guangming Wang;Xiyuan Lei;Wen Li;Won Hee Lee;Lianchi Huang;Jialin Zhu;Shanshan Jia;Dong Wang;Yang Zheng;Hua Zhang;Badong Chen;Gang Wang

{"title":"Channel-Selection-Based Temporal Convolutional Network for Patient-Specific Epileptic Seizure Detection","authors":"Guangming Wang;Xiyuan Lei;Wen Li;Won Hee Lee;Lianchi Huang;Jialin Zhu;Shanshan Jia;Dong Wang;Yang Zheng;Hua Zhang;Badong Chen;Gang Wang","doi":"10.1109/TCDS.2024.3433551","DOIUrl":"10.1109/TCDS.2024.3433551","url":null,"abstract":"Since sudden and recurrent epileptic seizures seriously affect people's lives, computer-aided automatic seizure detection is crucial for precise diagnosis and prompt treatment. A novel seizure detection algorithm named channel selection-based temporal convolutional network (CS-TCN) was proposed in this article. First, electroencephalogram (EEG) recordings were segmented into 2-s intervals and features were extracted from both the time and frequency domains. Then, the expanded fisher score channel selection method was employed to select channels that contribute the most to seizure detection. Finally, the features from selected EEG channels were fed into the TCN to capture inherent temporal dependencies of EEG signals and detect seizure events. Children Hospital Boston and Massachusetts Institute of Technology (CHB-MIT) and Siena datasets were used to verify the detection performance of the CS-TCN algorithm, achieving sensitivities of 98.56% and 98.88%, and specificities of 99.80% and 99.88% in samplewise analysis, respectively. In eventwise analysis, the algorithm achieved sensitivities of 97.57% and 95.00%, with delays of 6.91 and 18.62 s, and FDR/h of 0.11 and 0.39, respectively. These results surpassed state-of-the-art few-channel algorithms for both datasets. CS-TCN algorithm offers excellent performance while simplifying model complexity and computational requirements, thus showcasing its potential for facilitating seizure detection in home environments.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"179-188"},"PeriodicalIF":5.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784744","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

GLADA: Global and Local Associative Domain Adaptation for EEG-Based Emotion Recognition GLADA：基于脑电图的情绪识别的全局和局部关联域自适应

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-23 DOI: 10.1109/TCDS.2024.3432752

Tianxu Pan;Nuo Su;Jun Shan;Yang Tang;Guoqiang Zhong;Tianzi Jiang;Nianming Zuo

{"title":"GLADA: Global and Local Associative Domain Adaptation for EEG-Based Emotion Recognition","authors":"Tianxu Pan;Nuo Su;Jun Shan;Yang Tang;Guoqiang Zhong;Tianzi Jiang;Nianming Zuo","doi":"10.1109/TCDS.2024.3432752","DOIUrl":"10.1109/TCDS.2024.3432752","url":null,"abstract":"Emotion recognition based on electroencephalography (EEG) has significant advantages in terms of reliability and accuracy. However, individual differences in EEG limit the ability of sentiment classifiers to generalize across subjects. Furthermore, due to the nonstationarity of EEG, subject signals can vary with time, an important challenge for temporal emotion recognition. Several emotion recognition methods have been developed that consider the alignment of conditional distributions, but do not balance the weights of conditional and marginal distributions. In this article, we propose a novel approach to generalize emotion recognition models across individuals and time, i.e., global and local associative domain adaptation (GLADA). The proposed method consists of three parts: 1) deep neural networks are used to extract deep features from emotional EEG data; 2) considering that marginal and conditional distributions between domains can contribute to adaptation differently, a method that combines coarse-grained adversarial adaptation and fine-grained adversarial adaptation is used to narrow the domain distance of the joint distribution in the EEG data between subjects (i.e., reduce intersubject variability), and the weights of the marginal and conditional distributions are automatically balanced using dynamic balancing factors; and 3) domain adaptation is used to accelerate model convergence. Using GLADA, subject-independent EEG emotion recognition is improved by reducing the influence of the subject’s personal information on EEG emotion. Experimental results demonstrate that the GLADA model effectively addresses the domain transfer problem, resulting in improved performance across multiple EEG emotion recognition tasks.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"167-178"},"PeriodicalIF":5.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784822","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

A Derivative Topic Propagation Model Based on Multidimensional Cognition and Game Theory 基于多维认知和博弈论的衍生话题传播模型

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-22 DOI: 10.1109/TCDS.2024.3432337

Qian Li;Long Gao;Wenyi Xi;Tun Li;Rong Wang;Junwei Ge;Yunpeng Xiao

{"title":"A Derivative Topic Propagation Model Based on Multidimensional Cognition and Game Theory","authors":"Qian Li;Long Gao;Wenyi Xi;Tun Li;Rong Wang;Junwei Ge;Yunpeng Xiao","doi":"10.1109/TCDS.2024.3432337","DOIUrl":"10.1109/TCDS.2024.3432337","url":null,"abstract":"Given that emotional content spreads more widely than rational content in social networks, as well as the complexity of user cognition and the interaction of derivative topics, this article proposes a derivative topic dissemination model that integrates multidimensional cognition and game theory. First, regarding the issue of user emotional reactions in mining topics. In this article, we quantify the affective influence among users by considering user behaviors as continuous conversations through conversation-level sentiment analysis and the proximity centrality of social networks. Second, considering that user behavior is influenced by multidimensional cognition, this article proposes a method based on S(Sensibility) R(Rationality) 2vec to simulate the dialectical relationship between sensibility and rationality in the user decision-making process. Finally, considering the cooperative and competitive relationship among derived topics, this article uses evolutionary game theory to analyze the topic life cycle and quantify its impact on user behavior by time discretization method. Accordingly, we propose a CG-back-propagation (BP) model incorporating a BP neural network to efficiently simulate the nonlinear relationship of user behavior. Experiments show that the model can not only effectively tap the influence of multidimensional cognition on users’ retweeting behavior, but also effectively perceive the propagation dynamics of derived topics.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"189-204"},"PeriodicalIF":5.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784748","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

Speech Imagery Decoding Using EEG Signals and Deep Learning: A Survey 利用脑电信号和深度学习进行语音图像解码：调查

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-19 DOI: 10.1109/TCDS.2024.3431224

Liying Zhang;Yueying Zhou;Peiliang Gong;Daoqiang Zhang

{"title":"Speech Imagery Decoding Using EEG Signals and Deep Learning: A Survey","authors":"Liying Zhang;Yueying Zhou;Peiliang Gong;Daoqiang Zhang","doi":"10.1109/TCDS.2024.3431224","DOIUrl":"10.1109/TCDS.2024.3431224","url":null,"abstract":"Speech imagery (SI)-based brain–computer interface (BCI) using electroencephalogram (EEG) signal is a promising area of research for individuals with severe speech production disorders. Recent advances in deep learning (DL) have led to significant improvements in this domain. However, there is a lack of comprehensive review that covers the application of DL methods for decoding imagined speech via EEG. In this article, we survey SI and DL literature to address critical questions regarding preferred paradigms, preprocessing necessity, optimal input formulations, and current trends in DL-based techniques. Specifically, we first search major databases across science and engineering disciplines for relevant studies. Then, we analyze the DL-based techniques applied in SI decoding from five main perspectives: dataset, preprocessing, input formulation, DL architecture, and performance evaluation. Moreover, we summarize the key findings of this work and propose a set of practical recommendations. Finally, we highlight the practical challenges of DL-based imagined speech decoding and suggest future research directions.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"22-39"},"PeriodicalIF":5.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738941","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

Implementing Brain-Like Fear Generalization and Emotional Arousal Associated With Memory 实现与记忆相关的类脑恐惧泛化和情绪唤醒

IF 5 3区计算机科学

IEEE Transactions on Cognitive and Developmental Systems Pub Date : 2024-07-11 DOI: 10.1109/TCDS.2024.3425845

Mei Guo;Douyin Zhang;Wenhai Guo;Gang Dou;Junwei Sun

{"title":"Implementing Brain-Like Fear Generalization and Emotional Arousal Associated With Memory","authors":"Mei Guo;Douyin Zhang;Wenhai Guo;Gang Dou;Junwei Sun","doi":"10.1109/TCDS.2024.3425845","DOIUrl":"10.1109/TCDS.2024.3425845","url":null,"abstract":"Emotion plays an important role in human life. In recent years, memristor-based emotion circuits have been proposed extensively, but few circuits simulate the neural circuity that generates specific emotions in the limbic system. In this article, a memristor-based circuit of brain-like fear generalization is proposed. It is described from two dimensions of perception and higher cognition, respectively, both of which are realized by simulating the limbic system of human brain. The main difference between these two dimensions lies in the circuit design of the hippocampus module. Moreover, the memory enhancement effect caused by fear is one of the reasons for the phenomenon of fear generalization. That is, high arousal of fear leads to enhanced memory. Herein, the memristor-based circuit associated with different emotional arousal and memory is designed. The simulation results in SPICE show that the circuit is able to implement the brain-like fear generalization and the emotional memory under different arousal. The circuit design of these neural networks may provide some references for the field of brain-like robots.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"155-166"},"PeriodicalIF":5.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141612251","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