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Deep neurocomputational fusion for ASD diagnosis using multi-domain EEG analysis

Neurocomputing 2025年 641卷

作者： Rasool, Abdur Aslam, Saba Xu, Yongjie Wang, Yishan Pan, Yi Chen, Weiyang Department of Information and Computer Sciences University of Hawaii at Manoa HonoluluHI96822 United States Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institutes of Advanced Technology Guangdong Shenzhen518055 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Guangdong Shenzhen518055 China School of Cyber Science and Engineering Qufu Normal University Shandong Qufu273165 China

Autism spectrum disorder (ASD) presents significant challenges in early detection due to its heterogeneous nature and the subtlety of neurophysiological variations. Electroencephalography (EEG) has emerged as a promising tool for ASD diagnosis, offering insights into intricate neurophysiological signal dynamics. However, existing computational EEG analysis techniques often struggle to effectively capture complex ASD-related neural activity patterns. To address these limitations, this study proposes an Encoder-Ensemble Fusion Model (EEFM), designed to enhance EEG-based ASD classification through advanced neurocomputational modeling. EEFM integrates multi-domain EEG features with a sophisticated fusion network, initially extracting a diverse range of time and frequency-domain characteristics. This model enables the identification of nuanced and heterogeneous ASD-specific neural activity, leveraging ensemble learning and autoencoder architectures to uncover complex relationships within the data. Specifically, an LSTM (Long Short-Term Memory)-autoencoder models temporal and spatial dependencies, followed by an XGBoost Regressor for nonlinear EEG signal interpretation and a linear regression model for decision boundary refinement. The proposed model has been evaluated on a dataset focusing on sex-specific EEG variations in ASD and Typically Developing (TD) subjects. By incorporating EEG-based neurophysiological biomarkers, EEFM achieved 93% accuracy in distinguishing ASD from TD individuals. This model enhances sensitivity to subtle ASD-related abnormalities while addressing challenges in data heterogeneity, scarcity, and generalizability, contributing to advancements in neurocomputing methodologies for EEG-based ASD diagnosis. © 2025 Elsevier B.V.

关键词： Neurons

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Existence theorem of finite krasner hyperfields

arXiv

引用

arXiv 2021年

作者： Tsafack, Surdive Atamewoue Amassayoga, Ogadoa Onasanya, Babatunde O. Feng, Yuming School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing404100 China Department of Mathematics Higher Teacher Training College University of Yaounde 1 Cameroon Department of Mathematics Faculty of Science University of Yaounde 1 Cameroon Key Laboratory of Intelligent Information Processing and Control Chongqing Three China Gorges University Chongqing404100 China Department of Mathematics Uni-versity of Ibadan Nigeria School of Computer Science and Engineering Chongqing Three Gorges University China

关键词： Algebra

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Learning dynamical systems using local stability priors

arXiv

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arXiv 2020年

作者： Mehrjou, Arash Iannelli, Andrea Schölkopf, Bernhard Empirical Inference Department of Max Planck Institute for Intelligent Systems Tübingen Germany Department of Computer Science ETH Zürich Faculty of Electrical Engineering Zürich Switzerland Department of Electrical Engineering Automatic Control Lab ETH Zürich8092 Switzerland Empirical Inference Department of Max Planck Institute for Intelligent Systems Tübingen Germany

A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed. The nonlinear identification leverages the local stability information as a prior on the system, effectively endowing the estimate with this important structural property. In addition, the knowledge of the region of attraction plays an experiment design role by informing the selection of initial conditions from which trajectories are generated and by enabling the use of a Lyapunov function of the system as a regularization term. Numerical results show that the proposed method allows efficient sampling and provides an accurate estimate of the dynamics in an inner approximation of its region of attraction. Copyright © 2020, The Authors. All rights reserved.

关键词： Lyapunov functions

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DeepSeek or ChatGPT: Can brain-computer interfaces/brain-inspired computing achieve leapfrog development with large AI models?

Brain-X

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Brain-X 2025年第1期3卷

作者： Bai, Long Chen, Shugeng Wang, Peng Chen, He Yan, Jiaqing Zhu, Xiaojian Song, Enming Tian, Bobo Su, Jiacan Li, Xiaoli Organoid Research Center Institute of Translational Medicine Shanghai University Shanghai China Department of Orthopedics Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China National Center for Translational Medicine (Shanghai) SHU Branch Shanghai University Shanghai China Wenzhou Institute of Shanghai University Zhejiang Wenzhou China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Department of Language Literature and Communication Faculty of Humanities Vrije Universiteit Amsterdam Amsterdam Netherlands School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou China Pazhou Laboratory Guangdong Guangzhou China School of Electrical and Control Engineering North China University of Technology Beijing China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo China Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai China Key Laboratory of Polar Materials and Devices Department of Electronics Ministry of Education East China Normal University Shanghai China

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Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

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Nature Ecology and Evolution 2025年第6期9卷 1031-1042页

作者： Pringle, Stephen Dallimer, Martin Goddard, Mark A. Le Goff, Léni K. Hart, Emma Langdale, Simon J. Fisher, Jessica C. Abad, Sara-Adela Ancrenaz, Marc Angeoletto, Fabio Auat Cheein, Fernando Austen, Gail E. Bailey, Joseph J. Baldock, Katherine C. R. Banin, Lindsay F. Banks-Leite, Cristina Barau, Aliyu S. Bashyal, Reshu Bates, Adam J. Bicknell, Jake E. Bielby, Jon Bosilj, Petra Bush, Emma R. Butler, Simon J. Carpenter, Dan Clements, Christopher F. Cully, Antoine Davies, Kendi F. Deere, Nicolas J. Dodd, Michael Drinkwater, Rosie Driscoll, Don A. Dutilleux, Guillaume Dyrmann, Mads Edwards, David P. Farhadinia, Mohammad S. Faruk, Aisyah Field, Richard Fletcher, Robert J. Foster, Chris W. Fox, Richard Francksen, Richard M. Franco, Aldina M. A. Gainsbury, Alison M. Gardner, Charlie J. Giorgi, Ioanna Griffiths, Richard A. Hamaza, Salua Hanheide, Marc Hayward, Matt W. Hedblom, Marcus Helgason, Thorunn Heon, Sui P. Hughes, Kevin A. Hunt, Edmund R. Ingram, Daniel J. Jackson-Mills, George Jowett, Kelly Keitt, Timothy H. Kloepper, Laura N. Kramer-Schadt, Stephanie Labisko, Jim Labrosse, Frédéric Lawson, Jenna Lecomte, Nicolas de Lima, Ricardo F. Littlewood, Nick A. Marshall, Harry H. Masala, Giovanni L. Maskell, Lindsay C. Matechou, Eleni Mazzolai, Barbara McConnell, Alistair Melbourne, Brett A. Miriyev, Aslan Nana, Eric Djomo Ossola, Alessandro Papworth, Sarah Parr, Catherine L. Payo-Payo, Ana Perry, Gad Pettorelli, Nathalie Pillay, Rajeev Potts, Simon G. Prendergast-Miller, Miranda T. Qie, Lan Rolley-Parnell, Persie Rossiter, Stephen J. Rowcliffe, Marcus Rumble, Heather Sadler, Jon P. Sandom, Christopher J. Sanyal, Asiem Schrodt, Franziska Sethi, Sarab S. Shabrani, Adi Siddall, Robert Smith, Simón C. Snep, Robbert P. H. Soulsbury, Carl D. Stanley, Margaret C. Stephens, Philip A. Stephenson, P.J. Struebig, Matthew J. Studley, Matthew Svátek, Martin Tang, Gilbert Taylor, Nicholas K. Umbers, Kate D. L. Ward, Robert J. White, Patrick J. C. Whittingham, Mark J. Wich, Serge Williams, Christopher D. Yakubu, Ibrahim B. Yoh, Natalie Durrell Institute of Conservation and Ecology (DICE) School of Natural Sciences University of Kent Canterbury United Kingdom Centre for Environmental Policy Imperial College London London United Kingdom Department of Geography and Environmental Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom Synthotech Ltd Milner Court Hornbeam Square Harrogate United Kingdom Mechanical Engineering Department University College London London United Kingdom HUTAN SWD Kota Kinabalu Malaysia Programa de Pós-Graduação em Gestão e Tecnologia Ambiental da Universidade Federal de Rondonópolis Rondonópolis Brazil Department of Engineering Harper Adams University Newport United Kingdom Applied Ecology Research Group School of Life Sciences Anglia Ruskin University Cambridge United Kingdom Centre for Ecology and Hydrology Penicuik United Kingdom Department of Life Sciences Imperial College London Silwood Park Campus Ascot United Kingdom Department of Urban and Regional Planning Faculty of Earth and Environmental Sciences Bayero University Kano Nigeria Greenhood Nepal Kathmandu Nepal Animal Rural & Environmental Sciences Nottingham Trent University Nottinghamshire United Kingdom School of Biological and Environmental Sciences Liverpool John Moores University Liverpool United Kingdom Lincoln Centre for Autonomous Systems University of Lincoln Lincoln United Kingdom Royal Botanic Garden Edinburgh Edinburgh United Kingdom School of Biological Sciences University of East Anglia Norwich Research Park Norwich United Kingdom Digital Ecology Limited Bristol United Kingdom School of Biological Sciences University of Bristol Bristol United Kingdom Department of Computing Imperial College London London United Kingdom Department of Ecology and Evolutionary Biology University of Colorado Boulder CO United States Faculty of Science Technology Engineering

With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively. © The Author(s) 2025.

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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

Research Square

引用

Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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What Is Required to Prevent a Second Major Outbreak of SARS-CoV-2 upon Lifting Quarantine in Wuhan City,China

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The Innovation 2020年第1期1卷 152-157页

作者： Lei Zhang Mingwang Shen Xiaomeng Ma Shu Su Wenfeng Gong Jing Wang Yusha Tao Zhuoru Zou Rui Zhao Joseph T.F.Lau Wei Li Feng Liu Kai Ye Youfa Wang Guihua Zhuang Christopher K.Fairley China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’anShaanxi 710061PR China Melbourne Sexual Health Centre Alfred HealthMelbourneAustralia Central Clinical School Faculty of MedicineNursing and Health SciencesMonash UniversityMelbourneVICAustralia Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhou 450001 HenanChina Institute of Health Policy Management and Evaluation University of TorontoTorontoONCanada School of Public Health and Preventive Medicine Faculty of MedicineMonash UniversityMelbourneAustralia Centre for Research Behaviors Centre for Health Behaviours ResearchSchool of Public Health and Primary CareThe Chinese University of Hong KongHong KongHong Kong Bill and Melinda Gates Foundation SeattleWAUSA Department of Epidemiology and Health Statistics School of Public HealthSoutheast UniversityNanjing210009 JiangsuChina Shaanxi Provincial Center for Disease Control and Prevention Xi’an710054 ShaanxiChina MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’an 710049 China School of Automation Science and Engineering Faculty of Electronics and Information EngineeringXi’an Jiaotong UniversityXi’an 710049 China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061 China The School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049 China Global Health Institute Department of Epidemiology and BiostatisticsSchool of Public HealthXi’an Jiaotong University Health Science CenterXi’anShaanxi 710061China

BACKGROUND The Chinese government implemented a metropolitan-wide quarantine of Wuhan city on 23rd January 2020 to curb the epidemic of the coronavirus *** of this quarantine is *** modelled the effects of two key hea... 详细信息

关键词： Wuhan break Major

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Progressive Inertial Poser: Progressive Real-Time Kinematic Chain Estimation for 3D Full-Body Pose from Three IMU Sensors

arXiv

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arXiv 2025年

作者： Zhu, Zunjie Zhao, Yan Hu, Yihan Wang, Guoxiang Qiu, Hai Zheng, Bolun Yan, Chenggang Xu, Feng School of Communication Engineering Hangzhou Dianzi University Hangzhou323000 China Key Laboratory of Micro-nano Sensing and IoT of Wenzhou Wenzhou Institute of Hangzhou Dianzi University Wenzhou325038 China School of Control Science and Engineering Tiangong University Tianjin300387 China College of Business Lishui University Lishui310018 China Costar Intelligent Optoelectronics Technology Co. Ltd China School of Automation Hangzhou Dianzi University Hangzhou323000 China Faculty of Applied Sciences Macao Polytechnic University 999078 China School of software and BNRist Tsinghua University Beijing100084 China

The motion capture system that supports full-body virtual representation is of key significance for virtual reality. Compared to vision-based systems, full-body pose estimation from sparse tracking signals is not limited by environmental conditions or recording range. However, previous works either face the challenge of wearing additional sensors on the pelvis and lower-body or rely on external visual sensors to obtain global positions of key joints. To improve the practicality of the technology for virtual reality applications, we estimate full-body poses using only inertial data obtained from three Inertial Measurement Unit (IMU) sensors worn on the head and wrists, thereby reducing the complexity of the hardware system. In this work, we propose a method called Progressive Inertial Poser (ProgIP) for human pose estimation, which combines neural network estimation with a human dynamics model, considers the hierarchical structure of the kinematic chain, and employs a multi-stage progressive network estimation with increased depth to reconstruct full-body motion in real time. The encoder combines Transformer Encoder and bidirectional LSTM (TE-biLSTM) to flexibly capture the temporal dependencies of the inertial sequence, while the decoder based on multi-layer perceptrons (MLPs) transforms high-dimensional features and accurately projects them onto Skinned Multi-Person Linear (SMPL) model parameters. Quantitative and qualitative experimental results on multiple public datasets show that our method outperforms state-of-the-art methods with the same inputs, and is comparable to recent works using six IMU sensors. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual reality

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Progressive Inertial Poser: Progressive Real-Time Kinematic Chain Estimation for 3D Full-Body Pose from Three IMU Sensors

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhu, Zunjie Zhao, Yan Hu, Yihan Wang, Guoxiang Qiu, Hai Zheng, Bolun Yan, Chenggang Xu, Feng Hangzhou Dianzi University School of Communication Engineering Hangzhou 323000 China Wenzhou Institute of Hangzhou Dianzi University Key Laboratory of Micro-nano Sensing and IoT of Wenzhou Wenzhou 325038 China Tiangong University School of Control Science and Engineering Tianjin 300387 China Lishui University College of Business Lishui 310018 China Costar Intelligent Optoelectronics Technology Co. Ltd China Hangzhou Dianzi University School of Automation Hangzhou 323000 China Macao Polytechnic University Faculty of Applied Sciences 999078 Macao Tsinghua University School of software and BNRist Beijing 100084 China

关键词： full-body virtual representation IMU sensors kinematic chain motion capture neural network progressive estimation virtual reality

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