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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Cheng Fei Rui Li Li-Ming Zhao Wei-Long Zheng Bao-Liang Lu Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence 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

Decision confidence is an individual's feeling of correctness or optimization when making a decision. Various physiological signals, including electroencephalography (EEG) and eye movements have been studied extensively in measuring levels of decision confidence in humans. While multimodal fusion generally performs better than single-modal approaches, it requires data from different modalities at a greater cost. In particular, collection of EEG data is more complicated and time consuming while eye movement signals are much easier to acquire. To tackle this problem, we propose a cross-modal method based on generative adversarial learning. In our method, the intrinsic relationship between eye movement and EEG features in a high-level feature space can be learned in the training phase, and then we can obtain multimodal information during the test phase when only eye movements are available as inputs. Experimental results on the SEED-VPDC dataset demonstrate that our proposed method outperforms single-modal methods trained and tested only on eye movement signals with an improvement of approximately 5.43% in accuracy, and maintains competitive performance in comparison with multimodal methods. Our cross-modal approach requires only eye movements as inputs and reduces reliance on EEG data, making the decision confidence measurement more applicable and practicable.

关键词： Training Costs Neural engineering Particle measurements Generative adversarial networks Electroencephalography Adversarial machine learning

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Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models

arXiv

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arXiv 2024年

作者： Hafid, Abdelatif Rahouti, Mohamed Kong, Linglong Ebrahim, Maad Serhani, Mohamed Adel ESISA Analytica Laboratory Higher School of Engineering in Applied Sciences Fez24000 Morocco The Department of Computer & Information Science Fordham University New YorkNY10023 United States The Department of Mathematical and Statistical Sciences Alberta Machine Intelligence Institute University of Alberta EdmontonABT6G 2G1 Canada GAIA Ericsson Montreal Canada The Department of Information Systems University of Sharjah United Arab Emirates

Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remains a significant challenge. The volatile nature of the cryptocurrency market makes it even harder for traders and investors to make decisions. This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market, i.e., whether prices will increase or decrease. The model is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. We illustrate our approach with an empirical study of the closing price of Bitcoin. Several simulations, including a confusion matrix and Receiver Operating Characteristic curve, are used to assess the model’s performance, and the results show a buy/sell signal accuracy of over 92%. These findings demonstrate how machine learning models can assist investors and traders of cryptocurrencies in making wise/informed decisions in a very volatile market. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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MedRG: Medical Report Grounding with Multi-modal Large Language Model

arXiv

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arXiv 2024年

作者： Zou, Ke Bai, Yang Chen, Zhihao Zhou, Yang Chen, Yidi Ren, Kai Wang, Meng Yuan, Xuedong Shen, Xiaojing Fu, Huazhu National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Sichuan China College of Computer Science Sichuan University Sichuan China Institute of High Performance Computing A*STAR Singapore College of Intelligence and Computing Tianjin University Tianjin China Department of Radiology West China Hospital Sichuan University China Beth Israel Deaconess Medical Center Harvard Medical School Boston United States College of Mathematics Sichuan University Sichuan China

Medical Report Grounding is pivotal in identifying the most relevant regions in medical images based on a given phrase query, a critical aspect in medical image analysis and radiological diagnosis. However, prevailing visual grounding approaches necessitate the manual extraction of key phrases from medical reports, imposing substantial burdens on both system efficiency and physicians. In this paper, we introduce a novel framework—Medical Report Grounding (MedRG)—an end-to-end solution for utilizing a multi-modal Large Language Model to predict key phrase by incorporating a unique token, , into the vocabulary to serve as an embedding for unlocking detection capabilities. Subsequently, the vision encoder-decoder jointly decodes the hidden embedding and the input medical image, generating the corresponding grounding box. The experimental results validate the effectiveness of MedRG, surpassing the performance of the existing state-of-the-art medical phrase grounding methods. This study represents a pioneering exploration of the medical report grounding task, marking the first-ever endeavor in this domain. © 2024, CC BY.

关键词： Diagnosis

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Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices

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Biomedical Engineering Frontiers 2023年第1期4卷 3-20页

作者： Kairu Dong Wen-Che Liu Yuyan Su Yidan Lyu Hao Huang Nenggan Zheng John A.Rogers Kewang Nan College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058China National Key Laboratory of Advanced Drug Delivery and Release Systems Zhejiang UniversityHangzhou 310058China College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhou310027China Department of Gastroenterology Brigham and Women’s HospitalHarvard Medical SchoolBostonMA 02115USA College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310058China Qiushi Academy for Advanced Studies Zhejiang UniversityHangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310058China CCAI by MOE and Zhejiang Provincial Government(ZJU) Hangzhou 310027China Querrey Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Department of Biomedical Engineering Northwestern UniversityEvanstonIL 60208USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL 60208USA Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA Jinhua Institute of Zhejiang University Jinhua 321299China

Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and *** methods exist for recording large-scale electrophysiological signals from these *** approaches often lack,however,real-time,uninterrupted investigations due to their rigid constructs,geometric constraints,and mechanical mismatch in integration with soft *** recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability,offering unique capabilities for chronic,stable interrogation and stimulation of millimeter-scale animals and miniature tissue *** review summarizes the most advanced technologies for electrophysiological studies,based on methods of 3-dimensional flexible bioelectronics.A concluding section addresses the challenges of these devices in achieving freestanding,robust,and multifunctional biointerfaces.

关键词： offering recording millimeter

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Consistent Attack: Universal Adversarial Perturbation on Embodied vision Navigation

arXiv

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arXiv 2022年

作者： Ying, Chengyang Qiaoben, You Zhou, Xinning Su, Hang Ding, Wenbo Ai, Jianyong Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Beijing100084 China Peng Cheng Laboratory Guangdong Shenzhen518055 China SAIC AI Lab China

Embodied agents in vision navigation coupled with deep neural networks have attracted increasing attention. However, deep neural networks have been shown vulnerable to malicious adversarial noises, which may potentially cause catastrophic failures in Embodied vision Navigation. Among different adversarial noises, universal adversarial perturbations (UAP), i.e., a constant image-agnostic perturbation applied on every input frame of the agent, play a critical role in Embodied vision Navigation since they are computation-efficient and application-practical during the attack. However, existing UAP methods ignore the system dynamics of Embodied vision Navigation and might be sub-optimal. In order to extend UAP to the sequential decision setting, we formulate the disturbed environment under the universal noise δ, as a δ-disturbed Markov Decision Process (δ-MDP). Based on the formulation, we analyze the properties of δ-MDP and propose two novel Consistent Attack methods, named Reward UAP and Trajectory UAP, for attacking Embodied agents, which consider the dynamic of the MDP and calculate universal noises by estimating the disturbed distribution and the disturbed Q function. For various victim models, our Consistent Attack can cause a significant drop in their performance in the PointGoal task in Habitat with different datasets and different scenes. Extensive experimental results indicate that there exist serious potential risks for applying Embodied vision Navigation methods to the real world. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization

arXiv

引用

arXiv 2021年

作者： Huang, Feihu Wu, Xidong Hu, Zhengmian College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Miit Key Laboratory of Pattern Analysis and Machine Intelligence China Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States

In the paper, we propose a class of faster adaptive Gradient Descent Ascent (GDA) methods for solving the nonconvex-strongly-concave minimax problems by using the unified adaptive matrices, which include almost all existing coordinate-wise and global adaptive learning rates. In particular, we provide an effective convergence analysis framework for our adaptive GDA methods. Specifically, we propose a fast Adaptive Gradient Descent Ascent (AdaGDA) method based on the basic momentum technique, which reaches a lower gradient complexity of Õ(κ4ϵ-4) for finding an ϵ-stationary point without large batches, which improves the existing results of the adaptive GDA methods by a factor of O(√κ). Moreover, we propose an accelerated version of AdaGDA (VR-AdaGDA) method based on the momentum-based variance reduced technique, which achieves a lower gradient complexity of Õ(κ 4.5ϵ-3) for finding an ϵ-stationary point without large batches, which improves the existing results of the adaptive GDA methods by a factor of O(ϵ-1). Moreover, we prove that our VR-AdaGDA method can reach the best known gradient complexity of Õ(κ3ϵ-3) with the minibatch size O(κ3). The experiments on policy evaluation and fair classifier learning tasks are conducted to verify the efficiency of our new algorithms. © 2021, CC BY-NC-SA.

关键词： Gradient methods

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Kinetic and Kinematic Sensors-free Approach for Estimation of Continuous Force and Gesture in sEMG Prosthetic Hands

arXiv

引用

arXiv 2024年

作者： Liu, Gang Wang, Zhenxiang Xi, Chuanmei He, Ziyang Guo, Shanshan Zhang, Rui Yao, Dezhong School of Electrical and information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology Zhengzhou University Zhengzhou450001 China Engineering Department of International College Zhengzhou University 450001 China School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior Shanghai International Studies University China Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for NeuroInformation University of Electronic Science and Technology of China Chengdu China Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu China

Regression-based sEMG prosthetic hands are widely used for their ability to provide continuous kinetic and kinematic parameters. However, establishing these models requires complex sensors systems to collect corresponding kinetic and kinematic data in synchronization with sEMG, which is cumbersome and user-unfriendly. This paper proposes a kinetic and kinematic sensors-free approach for controlling sEMG prosthetic hands, enabling continuous decoding and execution of three hand movements: individual finger flexion/extension, multiple finger flexion/extension, and fist opening/closing. This approach utilizes only two data points (-1 and 1), representing maximal finger flexion force label and extension force label respectively, and their corresponding sEMG data to establish a near-linear model based on sEMG data and labels. The model's output labels values are used to control the direction and magnitude of fingers forces, enabling the estimation of continuous gestures. To validate this approach, we conducted offline and online experiments using four models: Dendritic Net (DD), Linear Net (LN), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The offline analysis assessed each model's ability to classify finger force direction and interpolate intermediate force values, while online experiments evaluated real-time control performance in controlling gestures and accurately adjusting forces. Our results demonstrate that the DD and LN models provide excellent real-time control of finger forces and gestures, highlighting the practical potential of this sensors-free approach for prosthetic applications. This study significantly reduces the complexity of collecting kinetic and kinematic parameters in sEMG-based regression prosthetics, thus enhancing the usability and convenience of prosthetic hands. © 2024, CC BY-NC-SA.

关键词： Multilayer neural networks

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Genetic and phenotypic relationships between coronary atherosclerotic heart disease and electrocardiographic traits

Genetic and phenotypic relationships between coronary athero...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinfeng Wang Haoyang Zhang Xuehao Xiu Mengling Qi Yuedong Yang Huiying Zhao Key Laboratory of Machine Intelligence and Advanced Computing of MOE School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Medical Research Center Sun Yat-sen Memorial Hospital Guangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Observational studies have revealed that Coronary Atherosclerotic Heart Disease (CAHD) is associated with abnormal electrocardiogram (ECG) traits. However, it remains unclear whether there are genetic correlations between ECG and CAHD. Here, we explored genetic correlations and putative causal relationships between CAHD and ECG by performing Mendelian randomization (MR) and Polygenic risk score (PRS) analyses on the summary statistics from a large-scale genome-wide association study (GWAS) for CAHD (FinnGen: Ncase 23363, Ncontrol 187840) and ECG traits (UK Biobank: Ncase=1137, Ncontrol=40823). Results showed a causal genetic relationship between CAHD and six ECG traits in the lead V6. These ECG traits combining with age and gender have predicted CAHD risk with an AUC of 0.76. Further summary data-based Mendelian randomization (SMR) analysis identified 11 risk genes associated with the causality between CAHD and ECG. Thus, the revealed putative causal effects of CAHD on ECG traits provide genetic evidence to support the importance of monitoring CAHD risk through the ECG.

关键词： Heart Correlation Genomics Atherosclerosis Electrocardiography Lead Bioinformatics

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Multidimensional analysis of sensory perception and neurophysiological responses to marinated beef cooked by diverse methods

引用

Innovative Food science and Emerging Technologies 2025年 103卷

作者： Yang, Tianyi Jiang, Wei Luo, Chencheng Hu, Jin Xu, Wei Zhang, Peng Jin, Xiaofei Zhou, Yuxuan Liu, Rui Qian, Zhiyu Yang, Yamin Department of Biomedical Engineering Key Laboratory of Multi-modal Brain-Computer Precision Drive Ministry of Industry and Information Technology Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Key Laboratory of Digital Medical Equipment and Technology of Jiangsu Province Nanjing University of Aeronautics and Astronautics Nanjing210016 China Program in Neuroscience & Behavioral Disorders Duke-NUS Medical School National University of Singapore Singapore169857 Singapore Department of Neurosurgery Huashan Hospital Shanghai Medical College Fudan University National Center for Neurological Disorders National Key Laboratory of Medical Neurobiology Institutes of Brain Science Shanghai Key Laboratory of Brain Function and Regeneration Institute of Neurosurgery Shanghai200040 China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing211166 China College of Food Science and Engineering Yangzhou University Yangzhou225127 China

The cooking process significantly influences consumer perception and preference for meat. This study systematically compares the sensory and neurophysiological responses elicited by beef processed via five cooking methods, including boiling (BO), sous-vide (SV), roasting (RO), pan-frying (PF), and deep-frying (DF). Sensory evaluations combining a check-all-that-apply (CATA) questionnaire and hedonic scaling revealed that RO, PF, and DF treatments produced richer flavors and more intense aromas, linked to higher hedonic ratings. In contrast, SV and BO yielded milder sensory profiles with lower flavor intensity and less consumer preference. Electroencephalography (EEG) analysis supported these findings by showing that high-heat methods elicited stronger neural responses, especially in the α, β, and γ frequency bands, which are associated with pleasure, appetite, and cognitive engagement, particularly prominent during olfactory and gustatory tests. Source localization further revealed greater activation in brain regions involved in sensory, reward, memory, emotion, and motivation for high-heat treated beef, demonstrating the connection between enhanced flavor complexity of high-temperature process and consumer appeal. Converging evidence from subjective sensory evaluation and neurophysiological analysis highlights the central role of thermal processing in enhancing food palatability and consumer satisfaction, offering practical insights for improving beef preparation in the food industry. © 2025 Elsevier Ltd

关键词： Neurons

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

引用

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Frank Kulwa Oluwarotimi Williams Samuel Mojisola Grace Asogbon Tolulope Tofunmi Oyemakinde Olumide Olayinka Obe Guanglin Li CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) Shenzhen Guangdong China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen Guangdong China School of Computing and Engineering University of Derby Derby United Kingdom Department of Computer Science Federal University of Technology Akure Nigeria

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.

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