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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.

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Augmented reality and robotics in education: A systematic literature review

Computers in Human Behavior: Artificial Humans

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computers in human Behavior: Artificial humans 2025年 4卷

作者： Christina Pasalidou Chris Lytridis Avgoustos Tsinakos Nikolaos Fachantidis Laboratory of Informatics & Robotic Applications in Education and Society Department of Educational and Social Policy University of Macedonia Thessaloniki Greece Human-Machines Interaction (HUMAIN) Lab Department of Computer Science Democritus University of Thrace Kavala Greece Advanced Educational Technologies and Mobile Applications (AETMA) Lab Department of Computer Science Democritus University of Thrace Kavala Greece

Integrating cutting-edge technologies into education has been a continuous goal to enhance teaching and learning experiences. Augmented Reality (AR) and robotics are two emerging technologies that have shown promise in transforming educational environments. This paper presents a systematic review of the literature on the combination of AR and robotics for educational purposes, identifying key applications, benefits, and trends. Using the PRISMA methodology, 69 relevant studies from five major databases were analysed and categorised into three themes: (a) AR and Socially Assistive Robots (SAR), (b) AR-assisted educational robotics, and (c) AR in robotics/engineering education. The review provides insights into how different AR-enhanced robotics applications across primary, secondary, and higher education, provide visualizations, multimodal feedback, and immersive experiences. Key findings suggest that while interactive features of AR and the embodiment of robots show promising results for learning, fostering motivation, excitement, positive attitudes, and enriched educational experiences, challenges such as technological complexity and cost remain barriers to widespread adoption. Future research should focus on pedagogical frameworks and large-scale implementations to optimize AR-robotics integration in diverse educational settings.

关键词： Robotics Augmented reality HRI Education

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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EEG-Eye Movements Cross-Modal Decision Confidence Measurement with Generative Adversarial Networks

EEG-Eye Movements Cross-Modal Decision Confidence Measuremen...

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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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Neural P3M: A Long-Range interaction Modeling Enhancer for Geometric GNNs

arXiv

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arXiv 2024年

作者： Wang, Yusong Cheng, Chaoran Li, Shaoning Ren, Yuxuan Shao, Bin Liu, Ge Heng, Pheng-Ann Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China University of Illinois Urbana-Champaign United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Microsoft Research AI4Science

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. © 2024, CC BY.

关键词： Graph neural networks

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Towards End-to-End Open Conversational Machine Reading

arXiv

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arXiv 2022年

作者： Zhou, Sizhe Ouyang, Siru Zhang, Zhuosheng Zhao, Hai UM-SJTU Joint Institute Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. Existing works generally utilize two independent modules to approach this problem’s two successive sub-tasks: first with a hard-label decision making and second with a question generation aided by various entailment reasoning methods. Such usual cascaded modeling is vulnerable to error propagation and prevents the two sub-tasks from being consistently optimized. In this work, we instead model OR-CMR as a unified text-to-text task in a fully end-to-end style. Experiments on the ShARC and OR-ShARC dataset show the effectiveness of our proposed end-to-end framework on both sub-tasks by a large margin, achieving new state-of-the-art results. Further ablation studies support that our framework can generalize to different backbone models. Copyright © 2022, The Authors. All rights reserved.

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Perception of Dynamic Displays by People with Argus® II Retinal Prostheses

Perception of Dynamic Displays by People with Argus® II Ret...

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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Breanne Christie Roksana Sadeghi Arathy Kartha Chigozie Ewulum Avi Caspi Francesco V. Tenore Roberta L. Klatzky Gislin Dagnelie Seth Billings Research and Exploratory Development Department Applied Physics Laboratory Johns Hopkins University Laurel MD USA Department of Biomedical Engineering Johns Hopkins School of Medicine Baltimore MD USA Department of Ophthalmology Johns Hopkins School of Medicine Baltimore MD USA Jerusalem College of Technology Jerusalem Israel Department of Psychology Carnegie Mellon University Pittsburgh PA USA Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA USA

Artificial vision can be elicited by stimulating the retina, which elicits flashes of light called phosphenes. For phosphenes to be utilized as part of a visual neuroprosthesis, images from one's visual surroundings must be recorded and translated into neural stimulation paradigms. While visual neuroprostheses have led to improvements in orientation and mobility for people with visual impairments, functionality is still limited. This is largely due to low resolution and a narrow field of view, and because artificial vision with current systems is so fundamentally different from natural vision. In this study, our goal was to evaluate if artificial vision could be delivered in a more strategic way for conveying information to visual prosthesis users. For example, blinking phosphenes could be used to signify imminent obstacles in one's surroundings. Four human participants who had Argus® II epiretinal prostheses performed a growing/shrinking object task and two variations of a blinking task. Just two out of the four participants could identify if a square pattern of phosphenes was growing or shrinking with better than chance accuracy. Only one individual could identify if a square/plus-sign pattern was growing or shrinking, with no participants achieving accuracy greater than 80%. All participants could determine whether a single virtual object was continuously visible or blinking at a rate of 1 Hz when stimulation pulse frequency was set to 6 Hz (accuracy $\geq 90\%$ ). When two virtual objects were shown, one blinking and one constant, at different ends of the visual field, only two participants could correctly report which side of the visual field contained the blinking stimulus. Based on these findings, we conclude that epiretinal neuroprostheses could incorporate single-object blinking as one form of visual feedback.

关键词： Visualization Image resolution Machine vision Visual impairment Neuroprostheses Neural engineering Visual prosthesis

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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Explaining the Black-box Smoothly - A Counterfactual Approach

arXiv

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arXiv 2021年

作者： Singla, Sumedha Eslami, Motahhare Pollack, Brian Wallace, Stephen Batmanghelich, Kayhan Computer Science Department The University of Pittsburgh PittsburghPA15206 United States School of Computer Science Human-Computer Interaction Institute Carnegie Mellon University United States Department of Biomedical Informatics The University of Pittsburgh PittsburghPA15206 United States University of Pittsburgh Medical School PittsburghPA15206 United States

We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest x-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metric

关键词： Medical applications

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Investigating the interaction Experience with Augmented Reality Based Writing Tools 21st

Investigating the Interaction Experience with Augmented Real...

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21st International Conference on human-computer interaction, HCI International 2019

作者： Zheng, Wei Yi, Xin Shi, Yuanchun Pervasive Human-Computer Interaction Group Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9783030235277

The contents of this paper include the design methodology of perceptual computing products, the innovative design of the AR writing and its technical feasibility, the writing interactive experience test of different hardware platforms, etc. It has certain reference value for the research work of paperless writing. © Springer Nature Switzerland AG 2019.

关键词： Augmented reality

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