检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Li, Shurui Daly, Ian Guan, Cuntai Cichocki, Andrzej Jin, Jing Center of Intelligent Computing School of Mathematics East China University of Science and Technology Shanghai200237 China Brain-Computer Interfacing Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Nanyang Technological University 639798 Singapore RIKEN Brain Science Institute Wako351–0198 Japan Torun87-100 Poland Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

A brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which require training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source Domain transfer based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for participant-independent ERP classification tasks. We train and validate our proposed model using both a publicly available OpenBMI dataset and a self-collected dataset, employing a leave-one-participant-out cross-validation scheme. Our experimental results demonstrate that our OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowCovNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method. © 2024, The Authors. All rights reserved.

关键词： brain computer interface

来源：评论

学校读者我要写书评

暂无评论

Assessment of causal effects of physical activity on neurodegenerative diseases:A Mendelian randomization study

引用

Journal of Sport and Health science 2021年第4期10卷 454-461页

作者： Peng-Fei Wu Hui Lu Xiaoting Zhou Xuchen Liang Ruizhuo Li Wan Zhang Danyang Li Kun Xia Center for Medical Genetics&Hunan Provincial Key Laboratory of Medical Genetics School of Life SciencesCentral South UniversityChangsha 410008China Department of Neurology Beth Israel Deaconess Medical Center&Harvard Medical SchoolBostonMA 02115USA Department of Neurology Xuanwu HospitalCapital Medical UniversityBeijing 100053China Department of Neurology Xiangya HospitalCentral South UniversityChangsha 410008China School of Physical Education Henan UniversityKaifeng 475001China School of Medicine South China University of TechnologyGuangzhou 510006China Department of Biology College of Arts&SciencesBoston UniversityBostonMA 02215USA CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China

Background:Physical activity has been hypothesized to play a protective role in neurodegenerative ***,effect estimates previously derived from observational studies were prone to confounding or reverse ***:We performed a two-sample Mendelian randomization(MR)analysis to explore the causal association of accelerometer-measured physical activity with 3 common neurodegenerative diseases:Alzheimer’s disease(AD),Parkinson’s disease(PD),and amyotrophic lateral sclerosis(ALS).We selected genetic instrumental variants reaching genome-wide significance(p<5×10^(-8))from 2 largest meta-analyses of about 91,100 UK Biobank *** statistics for AD,PD,and ALS were retrieved from the up-to-date studies in European ancestry led by the international *** random-effect,inverse-variance weighted MR was employed as the primary method,while MR pleiotropy residual sum and outlier(MR-PRESSO),weighted median,and MR-Egger were implemented as sensitivity *** statistical analyses were performed using the R programming language(Version 3.6.1;R Foundation for Statistical Computing,Vienna,Austria).Results:Primary MR analysis and replication analysis utilized 5 and 8 instrumental variables,which explained 0.2%and 0.4%variance in physical activity,*** each set,one variant at 17q21 was significantly associated with PD,and MR sensitivity analyses indicated them it as an outlier and source of heterogeneity and *** results with the removal of outlier variants suggested odds ratios(ORs)of neurodegenerative diseases per unit increase in objectively measured physical activity were 1.52 for AD(95%confidence interval(95%CI):0.88-2.63,p=0.13)and 3.35 for PD(95%CI:1.32-8.48,p=0.01),while inconsistent results were shown in the replication set for AD(OR=1.06,95%CI:1.01-1.12,p=0.02)and PD(OR=0.99,95%CI:0.88-0.12,p=0.97).Similarly,the beneficial effect of physical activity on ALS(OR=0.51,95%CI:0.29-0.91,p=0.02)was not confirmed in

关键词： Alzheimer’s disease Amyotrophic lateral sclerosis Genetic epidemiology Mendelian randomization Parkinson’s disease Physical activity

来源：评论

学校读者我要写书评

暂无评论

Data-Driven Output Containment Control of Heterogeneous Multiagent Systems: A Hierarchical Scheme

Data-Driven Output Containment Control of Heterogeneous Mult...

引用

IEEE Conference on Decision and Control

作者： Malika Sader Wenyu Li Yanhui Yin Zhongmei Li Dexian Huang Zhongxin Liu Xiao He Chao Shang Department of Automation Tsinghua University Beijing China College of Artificial Intelligence Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China School of Automation Engineering North-East Electric Power University Jilin China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Department of Automation Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this article, we propose a data-driven solution to the output containment control problem of multiagent systems characterized by heterogeneous and unknown dynamics. The proposed data-driven scheme is hierarchical, which includes a network layer and a physical layer, bypassing the traditional modeling exercise and eliminating the need for explicit state-space models. In the network layer, a fully distributed observer without knowing the dynamics of leaders is designed to generate a containment trajectory. A data-driven approach based on data sampled from an auxiliary system is developed to overcome the dependence on the explicit state-space models for design of control gains. This paves the way for deriving a datadriven solution to the regulator equation, which is derived by employing the data informativity condition and relevant data in the physical layer. The results demonstrate that the closedloop system is asymptotically stable, and the regulated output containment converges to zero. Compared to generic modelbased hierarchical schemes, the assumptions that the system matrices are completely known and homogeneous on system dynamics can be relaxed.

关键词： Regulators Protocols System dynamics Distributed feedback devices Observers Physical layer Mathematical models State-space methods Trajectory Multi-agent systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

Enhancing Motor Imagery EEG Signal Classification with Simpl...

引用

International Winter Workshop on brain-computer interface (BCI)

作者： Lu Wang Junkongshuai Wang Bo Wen Wei Mu Lusheng Liu Jiaguan Han Lihua Zhang Jie Jia Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical NeurobiologyAcademy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-MedicalFudan University Shanghai China Ji Hua Laboratory Guangdong Province Foshan China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

来源：评论

学校读者我要写书评

暂无评论

Tagging Continuous Labels for EEG-based Emotion Classification

Tagging Continuous Labels for EEG-based Emotion Classificati...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Rong-Fei Gu Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence the Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering and Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai People’s Republic of China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai People’s Republic of China

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Cross View Capture for Distributed Image Compression with Decoder Side Information

Cross View Capture for Distributed Image Compression with De...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yankai Yin Zhe Sun Peiying Ruan Feng Duan Ruidong Li Chi Zhu Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Institute of Natural Sciences Kanazawa University Ishikawa Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo University Chiba Japan NVIDIA AI Technology Center NVIDIA Japan Tokyo Japan Department of Systems Life Engineering Maebashi Institute of Technology Maebashi Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Image compression is increasingly important in applications like intelligent driving and smart surveillance systems. This study presents a novel cross view capture distributed image compression network (CVCDIC) to improve the compression quality by using decoder side information. The CVCDIC’s decoder utilizes feature extraction networks to extract features from both the primary image and the side information. Furthermore, a multi-level cross view attention module is designed to capture interrelated details between images at multiple hierarchical levels. Finally, a spatial refinement module, constructed on the foundation of information distillation networks, is designed to further refine the quality of reconstructed images. The results show that CVCDIC can achieve an MS-SSIM of 0.978 at 0.15 bpp, surpassing DSIN (0.925), NDIC (0.956), and ATN (0.955) on the KITTI Stereo dataset.

关键词： Measurement Image coding Fuses Surveillance Rate-distortion Feature extraction Decoding

来源：评论

学校读者我要写书评

暂无评论

猕猴脑网络组图谱:包含分区、连接和组织学的多层面全新大脑地图

引用

science Bulletin 2024年第14期69卷 2241-2259页

作者：陆玉恒崔玥曹龙董振伟程禄祺吴雯王昌硕刘新异刘有通张宝贵李德莹赵舶凯王海艳李开心马亮时维阳李雯马亚伟杜宗昌张佳琪熊辉罗娜刘妍妍侯肖逍韩景路孙洪吉蔡涛彭强冯琳清王骄健 George Paxinos 杨正宜樊令仲蒋田仔 Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering UniversityZhengzhou 450001China Neuroscience Research Australia and The University of New South Wales Sydney NSW 2031Australia Key Laboratory for NeuroInformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunming 650500China School of Mechanical and Power Engineering Harbin University of Science and TechnologyHarbin 150080China School of Life and Environmental Sciences Guilin University of Electronic TechnologyGuilin 541004China Sino-Danish College University of Chinese Academy of ScienceBeijing 100049China Research Center for Augmented Intelligence Zhejiang LabHangzhou 311100China Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital Yongzhou 425000China

The rhesus macaque(Macaca mulatta)is a crucial experimental animal that shares many genetic,brain organizational,and behavioral characteristics with humans.A macaque brain atlas is fundamental to biomedical and evolutionary ***,even though connectivity is vital for understanding brain functions,a connectivity-based whole-brain atlas of the macaque has not previously been *** this study,we created a new whole-brain map,the Macaque brainnetome Atlas(MacBNA),based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI *** new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional *** parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained *** a demonstrative application,the structural connectivity divergence between macaque and human brains was mapped using the brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain *** resulting resource includes:(1)the thoroughly delineated Macaque brainnetome Atlas(MacBNA),(2)regional connectivity profiles,(3)the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset(brainnetome-8),and(4)multi-contrast MRI,neuronal-tracing,and histological images collected from a single *** can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and ***,it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

关键词： Macaca mulatta brain atlas Connectivity-based parcellation Diffusion MRI Cytoarchitecture Cross-species comparison

来源：评论

学校读者我要写书评

暂无评论

Light Radiation Simulation of Micro-LED Array for the Optical Neural interface Targeted Cerebral Cortex in Rhesus Monkey

Light Radiation Simulation of Micro-LED Array for the Optica...

引用

International Winter Workshop on brain-computer interface (BCI)

作者： Lusheng Liu Wenlei Zhang Bo Wen Minjie Wang Aiping Wang Jing Zhang Yuan Zhang Song Le Haifeng Jiang Rongwei Zhai Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China Shanghai Mental Health Center Shanghai China Jiao Tong University School of Medicine Shanghai China Lingang Laboratory Shanghai China

The advanced technology of optogenetics has greatly promoted the development of neuroscience. To further optimize the optical neural interface, understanding the propagation process of light in neural tissues is essential for neuroscience research on the application of LED in optogenetics. In this work, we use TracePro software to build geometric models and simulate LED devices. The Rhesus monkey brain model is established to simulate the neural tissue to analyze the irradiance distribution at a specific location, to obtain the effective lighting range. In addition, we analyzed the influencing factors of the illumination range of the implanted light source device. We believe that the distance between devices should be $1.1 \sim1.2$ times of the light range of the simulation result when the devices fit the brain.

关键词： Neuroscience Shape Simulation Lighting Implants Light emitting diodes Optogenetics

来源：评论

学校读者我要写书评

暂无评论

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging

引用

Personality Neuroscience 2023年 6卷 e3-e3页

作者： Montag, Christian Klugah-Brown, Benjamin Zhou, Xinqi Wernicke, Jennifer Liu, Congcong Kou, Juan Chen, Yuanshu Haas, Brian W. Becker, Benjamin Department of Molecular Psychology Institute of Psychology and Education Ulm University Ulm Germany Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology Chengdu China Institute of Brain and Psychological Sciences Sichuan Normal University Chengdu China Department of Psychology Xinxiang Medical University Henan China Department of Psychology University of Georgia Athens GA United States

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-Thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be hardwired to the brain's neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Artificial intelligence MRI Trust

来源：评论

学校读者我要写书评

暂无评论

Distributed functional connectivity impairment in schizophrenia: A multi-site study 2

Distributed functional connectivity impairment in schizophre...

引用

2nd IET International Conference on Biomedical Image and Signal Processing, ICBISP 2017

作者： Yang, Yong Cui, Yue Xu, Kaibing Liu, Bing Song, Ming Chen, Jun Wang, Huiling Chen, Yunchun Guo, Hua Li, Peng Lu, Lin Lv, Luxian Wan, Ping Wang, Huaning Yan, Hao Yan, Jun Zhang, Hongxing Zhang, Dai Jiang, Tianzi Brainnetome Center Institute of Automation Chinese Academy of Sciences Beijing100190 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Radiology Renmin Hospital Wuhan University Wuhan430060 China Department of Psychiatry Renmin Hospital Wuhan University Wuhan430060 China Department of Psychiatry Xijing Hospital Fourth Military Medical University Xi'an710032 China Zhumadian Psychiatric Hospital Zhumadian463000 China Peking University Sixth Hospital Institute of Mental Health Beijing100191 China Key Laboratory of Mental Health Ministry of Health Peking University Beijing100191 China Department of Psychiatry Henan Mental Hospital Second Affiliated Hospital of Xinxiang Medical University Xinxiang453002 China Henan Key Lab of Biological Psychiatry Xinxiang Medical University Xinxiang453002 China Department of Psychology Xinxiang Medical University Xinxiang453002 China Center for Life Sciences PKU-IDG McGovern Institute for Brain Research Peking University Beijing100871 China Queensland Brain Institute University of Queensland BrisbaneQLD4072 Australia CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781785613180

Schizophrenia has been considered as a dysconneciton syndrome, which means the disintegration, or over interaction between brain regions may underlie the pathophysiology of this disease. Noninvasive techniques like functional magnetic resonance imaging (fMRI) were utilized to test this hypothesis. However, there is no consensus on which brain areas and which functional network is related with it, mostly due to the small sample size of previous studies. Supervised machine learning techniques are able to examine fMRI connectivity data in a multivariate manner and extract features predictive of group membership. This technique requires large sample sizes and results from small sample study may not generalize well. By applying a multi-task classification framework to large size multi-site schizophrenia resting functional MRI (rsMRI) dataset, we were able to find consistent and robust features. We observed that schizophrenia patients had widespread deficits in the brain. The most informative and robustly selected functional connectivity (FC) features were between and within functional networks such as the default mode network (DMN), the fronto-parietal control network (FPN), the subcortical network, and the cingulo-opercular task control network (CON). Our finding validated the dysconnection hypothesis of schizophrenia and shed light on the details of the impaired functional connectivity.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：