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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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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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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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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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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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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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A Simulation Study of Transcranial Magnetoacoustic Stimulation of the Basal Ganglia Thalamic Neural Network to Improve Parkinson's Disease Status

SSRN

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SSRN 2023年

作者： Zhang, Yanqiu Zhang, Hao Xu, Tianya Liu, Jiahe Mu, Jiayang Chen, Rongjie Yang, Jiumin Wang, Peiguo Jian, Xiqi School of Biomedical Engineering and Technology Tianjin Medical University Tianjin300070 China Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin300072 China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin300392 China Neurology Department Tianjin Huanhu Hospital Tianjin300350 China Department of Oncology Radiotherapy Cancer Institute Hospital of Tianjin Medical University Tianjin300202 China

Background: Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively. Objective: To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage. Methods: The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network. Results: When the stimulating effect of ultrasound is ignored, TMAS-STN suppresses the beta band amplitude of each nucleus, whereas TMAS-GPi affects only some nuclei. TMAS-STN&GPi can reduce the beta band amplitude. TMAS-STN&GPi&GPe is more effective. When considering the stimulatory effect of ultrasound, the stimulatory effect of lower sound pressures is negligible, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents. Conclusions: TMAS stimulation of multinuclear with appropriate ultrasound intensity is effective in PD and may be clinically useful. © 202

关键词： Ultrasonics

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Automatic Construction of Parallel Algorithm Portfolios for Multi-objective Optimization

arXiv

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arXiv 2022年

作者： Ma, Xiasheng Liu, Shengcai Hong, Wenjing School of Mechanical Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of Hand-Eye-Brain Interaction Beijing100083 China Singapore Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

It has been widely observed that there exists no universal best Multi-objective Evolutionary Algorithm (MOEA) dominating all other MOEAs on all possible Multi-objective Optimization Problems (MOPs). In this work, we advocate using the Parallel Algorithm Portfolio (PAP), which runs multiple MOEAs independently in parallel and gets the best out of them, to combine the advantages of different MOEAs. Since the manual construction of PAPs is non-trivial and tedious, we propose to automatically construct high-performance PAPs for solving MOPs. Specifically, we first propose a variant of PAPs, namely MOEAs/PAP, which can better determine the output solution set for MOPs than conventional PAPs. Then, we present an automatic construction approach for MOEAs/PAP with a novel performance metric for evaluating the performance of MOEAs across multiple MOPs. Finally, we use the proposed approach to construct a MOEAs/PAP based on a training set of MOPs and an algorithm configuration space defined by several variants of NSGA-II. Experimental results show that the automatically constructed MOEAs/PAP can even rival the state-of-the-art ensemble MOEAs designed by human experts, demonstrating the huge potential of automatic construction of PAPs in multi-objective optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Parallel algorithms

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Algorithmic Mirror: Designing an Interactive Tool to Promote Self-Reflection for YouTube Recommendations

arXiv

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arXiv 2025年

作者： Kondo, Yui Dunnell, Kevin Xiao, Qing Zhao, Jun Rocher, Luc Oxford Internet Institute University of Oxford United Kingdom MIT Media Lab Massachusetts Institute of Technology United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science University of Oxford United Kingdom

Big Data analytics and Artificial Intelligence systems derive non-intuitive and often unverifiable inferences about individuals’ behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value, these systems erode the intuitive connection between our actions and how we are perceived, diminishing control over our digital identities. While Explainable Artificial Intelligence scholars have attempted to explain the inner workings of algorithms, their visualizations frequently overwhelm end-users with complexity. This research introduces ‘hypothetical inference’—a novel approach that uses language models to simulate how algorithms might interpret users’ digital footprints and infer personal characteristics without requiring access to proprietary platform algorithms. Through empirical studies with fourteen adult participants, we identified three key design opportunities to foster critical algorithmic literacy: (1) reassembling scattered digital footprints into a unified map, (2) simulating algorithmic inference through LLM-generated interpretations, and (3) incorporating temporal dimensions to visualize evolving patterns. This research lays the groundwork for tools that can help users recognize the influence of data on platforms and develop greater autonomy in increasingly algorithm-mediated digital environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Video analysis

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Towards expert gaze modeling and recognition of a user’s attention in realtime

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Procedia computer science 2020年 176卷 2020-2029页

作者： Nora Castner Lea Geßler David Geisler Fabian Hüttig Enkelejda Kasneci Human-Computer Interaction University of Tübingen Germany Institute of Computer Science University of Tübingen Germany Department of Prosthodontics University Hospital Tübingen Germany

One of the appealing areas of expertise research is devoted to measuring the effectiveness of training programs for novices. With recent progress in eye tracking, gaze-based interaction systems recognize a user’s attention and can direct it accordingly. Moreover, dynamic visualization of an expert gaze model facilitates novice training by guiding the gaze to relevant areas. In addition, the system should be aware of realtime attention to remove an overlay that could occlude relevant information. We use an implementation of subtle gaze direction (SGD) and the simplified scanpath of a dentist to train naive participants in finding anomalies in dental radiographs. We were able to effectively direct user gaze to relevant image features without occluding the area when attention was recognized. Additionally, participants reported that the intervention was helpful for image inspection. The results of the model intervention show minimal improvements in anomaly detection, which is expected of naive subjects. We advocate that the system has the potential to be highly effective for advanced students and trainees with a certain foundation of conceptual knowledge.

关键词： Eye Tracking Gaze-based interaction Attention Guiding Expertise Learning

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