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Exploiting the Intrinsic Neighborhood Semantic Structure for Domain Adaptation in EEG-based Emotion Recognition

IEEE Transactions on Affective Computing 2025年

作者： Yang, Yi Wang, Ze Song, Yu Jia, Ziyu Wang, Boyu Jung, Tzyy-Ping Wan, Feng Macau University of Science and Technology Macao Centre for Mathematical Sciences Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications Faculty of Innovation Engineering 999078 China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin Key Laboratory of New Energy Power Conversion Transmission and Intelligent Control Tianjin300384 China Chinese Academy of Sciences Beijing Key Laboratory of Brainnetome and Brain-Computer Interface and Brainnetome Center Institute of Automation Beijing100045 China Western University Department of Computer Science Brain Mind Institute LondonONN6A 3K7 Canada University of California at San Diego Swartz Center for Computational Neuroscience Institute for Neural Computation La Jolla CA92093 United States University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China University of Macau Centre for Cognitive and Brain Sciences Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation 999078 China

Due to the inherent non-stationarity and individual differences present in electroencephalogram (EEG) signals, developing a generalizable model that performs well on new subjects is challenging in EEG-based emotion recognition. Most existing domain adaptation (DA) methods typically mitigate these discrepancies by aligning the marginal distributions of domain feature representations. However, when there is a significant difference in the class-conditional distribution between domain features and labels, the domain-invariant features learned by aligning marginal distributions may have limited discriminative ability for unlabeled target instances or even prove counterproductive. To address this issue, we propose a Neighborhood Semantic Aware Learning-based Dynamic Graph Attention Convolution (NSAL-DGAT) approach that learns target semantic information by considering the inter-domain semantic topological structure, thereby improving classifier adaptation for target instances. Specifically, the proposed NSAL framework is designed to capitalize on the insight that after domain feature alignment, some target samples and their neighboring source samples exhibit similar semantics. By leveraging the neighborhood topological structure, we extract and incorporate semantic target features to train a more transferable classifier. Besides, we implement an entropy weighting mechanism to emphasize representative target semantic information, encouraging target instances to prioritize high-confidence individuals within the source neighborhood. We have conducted extensive experiments on the public SEED dataset and our collected the Hearing-Impaired EEG dataset (HIED). The experimental results underscore the efficacy of our proposed NSAL-DGAT approach, showcasing state-of-the-art accuracy in subject-dependent as well as subject-independent scenarios. The source code is available at https://***/YYingDL/NSAL-DGAT. © 2010-2012 IEEE.

关键词： Speech analysis

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An Explainable and Lightweight Deep Learning Model with Attention Mechanism for Efficient Lung Disease Detection

An Explainable and Lightweight Deep Learning Model with Atte...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qian Tingting Sohaib Asif Yuke Lin Jincao Yao Enyu Wang Vicky Yang Wang Dong Xu Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou China Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou China Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou China Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Chinese Academy of Sciences China Zhejiang Chinese Medical University Hangzhou China Department of Radiology Imaging Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

COVID-19 has rapidly spread across the world as an extremely contagious disease. Therefore, early detection of the virus is essential to effectively control its transmission and prevent further spread. It is widely reported in the literature that convolutional neural networks (CNNs) are commonly utilized for COVID-19 detection. However, the majority of these models require substantial computational resources and possess a large number of parameters, rendering them less feasible for deployment on real-time devices. This study proposes a solution to address COVID-19 detection challenges by introducing LWIA-Net, a rapid and highly efficient lightweight CNN architecture. The LWIA-Net is equipped with channel attention squeeze and excitation (SE) blocks, as well as a naïve inception module, to enhance the network’s learning ability, reduce computational complexity, and maintain a high level of detection performance. By incorporating the naïve inception module, the LWIA-Net enables multi-level feature extraction, while the inclusion of the SE block helps focuses on informative channels. This integration contributes to the production of high-quality image features and effectively reduces redundancy. We performed thorough experiments using both our locally developed dataset and a publicly available dataset. The statistical analysis demonstrate that LWIA-Net achieves superior performance compared to pre-trained CNN architectures, despite its extremely lightweight architecture which demands low computational cost, memory space, and consists of only 1.11 million parameters. We conducted a comprehensive ablation study to gain a deeper understanding of the significance of each component and to verify their individual contributions to the LWIA-Net. Additionally, we utilized Grad-CAM analysis to confirm that the proposed model effectively identifies and emphasizes the most critical regions of interest. Experimental results have shown that LWIA-Net achieves remarkable accuracy rate

关键词： COVID-19 Statistical analysis Computational modeling Pulmonary diseases Redundancy Computer architecture Production Rendering (computer graphics) Real-time systems Convolutional neural networks

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Analysis of 5G academic Network based on graph representation learning method

arXiv

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arXiv 2021年

作者： Li, Xiaoming Xu, Guangquan Yu, Wei Jiao, Pengfei Song, Xiangyu Zhejiang Yuexiu University of Foreign Languages School of International Business Shaoxing China Shaoxing Key Laboratory of Intelligent Monitoring and Prevention of Smart City Shaoxing China School of Big Data School of Big Data Qingdao Huanghai University Qingdao China College of Intelligence and Computing Tianjin University Tianjin China School of IT Faculty of Science Engineering and Built Environment Deakin University GeelongVIC3220 Australia

With the rapid development of 5th Generation Mobile Communication Technology (5G), the diverse forms of collaboration and extensive data in academic social networks constructed by 5G papers make the management and analysis of academic social networks increasingly challenging. Despite the particular success achieved by representation learning in analyzing academic and social networks, most present presentation learning models focus on maintaining the first-order and second-order similarity of nodes. They rarely possess similar structural characteristics of spatial independence in the network. This paper proposes a Low-order Network representation Learning Model (LNLM) based on Non-negative Matrix Factorization (NMF) to solve these problems. The model uses the random walk method to extract low-order features of nodes and map multiple components to a low-dimensional space, effectively maintaining the internal correlation between members. This paper verifies the performance of this model, conducts comparative experiments on four test datasets and four real network datasets through downstream tasks such as multi-label classification, clustering, and link prediction. Comparing eight mainstream network representation learning models shows that the proposed model can significantly improve the detection efficiency and learning methods and effectively extract local and low-order features of the network. © 2021, CC BY.

关键词： Non-negative matrix factorization

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Single-cell technologies:From research to application

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The Innovation 2022年第6期3卷 92-108页

作者： Lu Wen Guoqiang Li Tao Huang Wei Geng Hao Pei Jialiang Yang Miao Zhu Pengfei Zhang Rui Hou Geng Tian Wentao Su Jian Chen Dake Zhang Pingan Zhu Wei Zhang Xiuxin Zhang Ning Zhang Yunlong Zhao Xin Cao Guangdun Peng Xianwen Ren Nan Jiang Caihuan Tian Zi-Jiang Chen Biomedical Pioneering Innovation Centre(BIOPIC) Peking UniversityBeijing 100871China Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthChinese Academy of SciencesShanghai 200031China School of Chemical Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China Mozhuo Biotech(Zhejiang)Co. Ltd.TongxiangJiaxing 314500China Geneis(Beijing)Co. Ltd.Beijing 100102China Guangzhou Institutes of Biomedicine and Health Chinese Academy of SciencesGuangzhou 510530China Department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China School of Food Science and Technology Dalian Polytechnic UniversityDalian 116034China State Key Laboratory of Transducer Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationBeijing Advanced Innovation Center for Biomedical EngineeringSchool of Engineering MedicineBeihang UniversityBeijing 100083China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714China Center of Peony Institute of Vegetables and FlowersChinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Flower Crops(North China)Key Laboratory of Biology and Genetic Improvement of Horticultural CropsMinistry of Agriculture and Rural AffairsBeijing 100081China Department of Hepatic Surgery Fudan University Shanghai Cancer CenterShanghai 200032China Advanced Technology Institute University of SurreyGuildfordSurreyGU27XHUK National Physical Laboratory TeddingtonMiddlesex TW110LWUK Institute of Clinical Science Zhongshan HospitalFudan UniversityShanghai 200032China West China School of Basic Medical Sciences&Forensic Medicine Sichuan UniversityChengdu 610041China Jinfeng Laboratory Chongqing 401329China Center for Reproduc

In recent years,more and more single-cell technologies have been developed.A vast amount of single-cell omics data has been generated by large projects,such as the Human Cell Atlas,the Mouse Cell Atlas,the Mouse RNA Atlas,the Mouse ATAC Atlas,and the Plant Cell *** on these single-cell big data,thousands of bioinformatics algorithms for quality control,clustering,cell-type annotation,developmental inference,cell-cell transition,cell-cell interaction,and spatial analysis are *** powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence,the molecular landscape at the single-cell level can be revealed.

关键词： Atlas landscape application

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Two-Month Cognitive Changes Enhance Prediction of Nonremission in Clinical High-Risk Individuals

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Biological Psychiatry 2025年第2期98卷 175-185.0页

作者： Zhang, TianHong Wei, YanYan Tang, XiaoChen Xu, LiHua Cui, HuiRu Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Tang, YingYing Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Longitudinal changes in cognitive function may be crucial in predicting clinical outcomes in clinical high-risk (CHR) individuals. In this study, we aimed to investigate the predictive value of baseline cognitive impairment and short-term cognitive changes for nonremission and conversion to psychosis in individuals at CHR for psychosis compared with healthy control individuals (HCs). Methods: This study used a multiple-group prospective design with a 3-year follow-up. CHR individuals and HCs were assessed at baseline and at a 2-month follow-up. Neuropsychological performance was evaluated using the Chinese version of the MATRICS (Measurement and Treatment Research to Improve Cognition in Schizophrenia) Consensus Cognitive Battery. Results: The study included 310 CHR individuals and 93 HCs. Significant improvements in predicting nonremission in CHR individuals were observed when incorporating cognitive changes over 2 months (area under the receiver operating characteristic curve [AUC] for baseline cognition, 0.690;AUC for changes, 0.819;z = 3.365, p < .001). key predictors included the Hopkins Verbal Learning Test–Revised (β = 0.083, p = .003), Wechsler Memory Scale-III spatial span (β = 0.330, p < .001), and Brief Visuospatial Memory Test–Revised (β = 0.127, p < .001). Conversely, predicting conversion to psychosis showed no significant difference between baseline and 2-month cognitive changes (AUC for baseline cognition, 0.667;AUC for changes, 0.666;z = 0.021, p = .242). Conclusions: The findings underscore the importance of dynamic cognitive monitoring in CHR individuals. Short-term cognitive changes significantly enhanced the prediction of nonremission but did not add predictive value for conversion to psychosis beyond baseline assessments. Specific cognitive domains, such as verbal learning and working memory, were particularly valuable for predicting clinical outcomes. © 2025 Society of Biological Psychiatry

关键词： Cognition Functional outcome Prodromal psychosis Remission Transition Ultra-high risk

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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An Original Design of robotic-Assisted Flexible Nasotracheal Intubation System

An Original Design of Robotic-Assisted Flexible Nasotracheal...

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IEEE International Conference on robotics and Biomimetics

作者： He Liu Shi-Qi Liu Xiao-Liang Xie Ye Li Xiao-Hu Zhou Zhen-Qiu Feng Guo-Tao Li Xi-Yao Ma Zeng-Guang Hou Yong-Yi Yuan Yong Cui Yu-Hong Tian Pu Dai State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China College of Otolaryngology Head and Neck Surgery The Sixth Medical Center of PLA General Hospital Beijing China Department of Otolaryngology The 983th Hospital of Joint Logistic Support Force Tianjin China

Nasotracheal intubation is a crucial emergency procedure that can improve the survival rate of injured patients. Compared to orotracheal intubation, nasotracheal intubation offers greater stability and comfort. However, due to the large curvature of the cavity, the procedure is highly challenging and requires skilled medical staff, which can be a shortage in emergencies and delay patient rescue. To solve this problem, a flexible and portable robot for nasotracheal intubation has been developed in this paper. The robot incorporates a continuum catheter, allowing the shape of the endotracheal tube’s distal section to be controllable and making it easier to pass through the nasal cavity into the trachea. An endoscope is integrated into the distal section of the catheter to make the intubation process more intuitive, allowing doctors to observe the injuries through endoscopic images and adjust the treatment plan. Additionally, the robot is designed with a quick-detachable structure, enabling aseptic and low-cost intubation procedures. The structure and circuitry of the robot are designed for portability, with a smaller size and lower power consumption, which can work for a long time just powered by batteries. Finally, experiments on a mannequin validate that the robotic system effectively reduces the difficulty of tracheal intubation, enabling even non-medically trained operators to complete the procedure with the assistance of the robot.

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Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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Construction of joint simulation platform and model identification for water storage air conditioning system

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Journal of Computers (Taiwan) 2020年第2期31卷 20-27页

作者： Wang, Yong-Shun Wei, Dong Liu, Kun Department of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Beijing Key Laboratory of Intelligent Processing for Building Big Data China

The predictive model is a main topic in the modeling predictive control(MPC) of the HVAC control field. According to Shenzhen Guoying building, the water storage air conditioning system was studied and modeled. This paper proposes a modeling method that combines the TRNSYS and MATLAB to model the water storage air conditioning system. The MPC method demonstrates good performance on the control of the water storage air conditioning system. And the MPC method requires a predictive model of the controlled object. So this paper focuses on the modeling of the water storage air conditioning system. A joint simulation platform of TRNSYS and MATLAB is built. Using rapid development function of TRNSYS build the models of the water tank and plate heat exchangers. The model of the water storage air conditioning system is identified by using artificial neural networks and the collected operation data through the joint simulation platform. Simulation results show that the prediction model match well the test data. The model identified has high accuracy and strong generalization ability. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Simulation platform

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On Pareto-frontier Approximate Computing for Many-core Systems

On Pareto-frontier Approximate Computing for Many-core Syste...

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intelligent Technology and Embedded Systems (ICITES), International Conference on

作者： Xinyue Hou Xiaohang Wang Maurizio Palesi Amit Kumar Singh Yingtao Jiang Mei Yang Letian Huang Junying Chen Key Laboratory of Big Data and Intelligent Robot (South China University of Technology) Ministry of Education School of Software Engineering South China University of Technology University of Catania Catania Italy School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom University of Nevada Las Vegas USA School of Electronic Science and Engineering University of Electronic Science and Technology of China China

Approximate computing is an emerging paradigm that aggressively improves performance or reduces energy consumption by sacrificing computation quality for error forgiving applications. Various approximate techniques, including loop truncation, approximate communication, etc. have been proposed. Previous works focus on optimization using only one approximation knob. However, we have observed that simultaneously optimizing with multiple approximation knobs leads to a large search space and is more likely to find better solutions. Therefore, in this paper, we first develop application models for performance, error, and power, followed by formulation of an optimization problem to maximize system performance under error and power constraints, using three approximation knobs, which are loop truncation, data dropping, and computational precision scaling. In order to solve the problem efficiently, a lightweight algorithm based on interior point algorithm is proposed. Experimental results show that, compared to state-of-the-art approximate approaches, the proposed scheme can reduce the execution time by as much as 33.1%. The overhead of the proposed method is low, making it a suitable approximate scheme for future many-core systems.

关键词： Energy consumption Power demand Embedded systems Computational modeling System performance Approximate computing Approximation algorithms

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