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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Xuan Liu, Shiwei Wang, Xinsheng Wan, Xiaohua Zhang, Fa Donghua University School of Computer Science and Technology Shanghai China Key Laboratory of Brain Health Intelligent Evaluation and Intervention Beijing China Beijing Institute of Technology School of Medical Technology Beijing China

ISBN: (纸本)9798350386226

Cryo-electron tomography (Cryo-ET) and subtomogram averaging techniques are highly effective in revealing high-resolution molecular structures. In this technique, accurate Contrast Transfer Function (CTF) correction is essential, as it profoundly affects image quality. This impact arises from various factors, including electron beam characteristics, sample thickness, diffraction phenomena, and lens aberrations. Traditional two-dimensional CTF(2D-CTF) correction methods become inadequate when Cryo-ET is increasingly applied to thicker samples and multi-angle imaging scenarios, since variations in defocus along the tilt axis lead to resolution deterioration. To address this challenge, a novel methodology, termed the Focus Defocus Changes Contrast Transfer Function (FDC-CTF), is put forward as a new type of three-dimensional Contrast Transfer Function (3D-CTF) method. This method geometrically determines the degree of particle defocus based on their coordinates within tomographic slices, effectively tackling the defocus gradient issues induced by sample thickness and tilting. Furthermore, a new workflow is proposed where, after obtaining the particle coordinates, each particle undergoes individual 3D-CTF correction, thereby enhancing the overall accuracy and efficiency of the process. We use two datasets to demonstrate the effectiveness of our method, showcasing its superior performance in alignment accuracy compared to existing methods. It provides a powerful solution for tomographic reconstruction. © 2024 IEEE.

关键词： Electron beam computed tomography

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Research on EEG Signal Classification Method Based on brain-computer interface Control of Rehabilitation Robot 17

Research on EEG Signal Classification Method Based on Brain-...

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17th International Convention on Rehabilitation Engineering and Assistive technology, i-CREATe 2024

作者： Zhang, Yiwen Wang, Wenzhi Liu, Gan Qiao, Yi Shi, Ruochuan Duan, Yahan Du, Ying Li, Sining Guo, Fengrui McHugh, Thomas John Duan, Feng Nankai University Tianjin key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation No.38 Tongyan Road Tianjin300350 China Riken Center for Brain Science Laboratory for Circuit and Behavioral Physiology Wakoshi Saitama Japan Graduate School of Arts and Sciences The University of Tokyo Department of Life Sciences Tokyo Japan

ISBN: (纸本)9798350355154

brain-computer interface (BCI) control of multiple rehabilitation robots provides a novel type of human-robot interaction and an important research direction in the field of intelligent rehabilitation. However, most current EEG signal classification algorithms for controlling rehabilitation robots exhibit several drawbacks, including a lack of novelty, insufficient classification accuracy and poor adaptability to different experimental paradigms. Therefore, we designed a new deep-learning method to classify EEG signals to enhance the effectiveness of BCI control of rehabilitation robots. Here we apply this method to classify EEG signals and control the rehabilitation robot to accomplish a target task. In addition, we compare our algorithm with several different EEG signal classification algorithms in two experimental paradigms: motor imagery and motor execution. Our deep learning method has the highest offline accuracy of 72.7% and an average accuracy of 72.4% under the motor imagery experimental paradigm, and the highest offline accuracy of 97.5% and an average accuracy of 90.4% under the motor execution experimental paradigm. These results show that our EEG signal classification algorithms is more effective compared to other EEG signal classification algorithms and can effectively be used to control a rehabilitation robot across a wider range of applications. © 2024 IEEE.

关键词： Human robot interaction

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Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy

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Heliyon 2024年第19期10卷 e38113页

作者： Huang, Yinggang Yang, Lifang Yang, Long Xu, Zehua Li, Mengmeng Shang, Zhigang School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China

Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking. © 2024 The Author(s)

关键词： Behavioral pre-control Parameter adaptive strategy Path tracking control Pigeon robot Stimulus model

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MyRoom: A Unity Plugin for Procedural and Interactive Indoor Scene Synthesis 5

MyRoom: A Unity Plugin for Procedural and Interactive Indoor...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Du, Haocheng Zhao, Yunlong Huang, Shuo Bai, Jiayu Tian, Song Liu, Jialin Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Center for the Humanities Shenzhen518055 China

ISBN: (纸本)9798350322774

The demand for indoor synthesis has increased significantly in recent years because of the emergence of computational design. This work designs and develops MyRoom, a Unity plugin that can import layout datasets for indoor synthesis and procedurally generate and interactively design indoor scenes. MyRoom enables users to easily edit and visualize non-intuitive layout description data, making it easier to generate and manipulate indoor scenes in Unity. MyRoom provides a user-friendly interface and powerful tools for designing and optimizing indoor layouts, including an automatic layout generator, making it ideal for game developers, interior designers, and researchers in the digital world-building industry. With MyRoom, users can streamline the process of creating high-quality indoor scenes in games and achieve their design goals efficiently. © 2023 IEEE.

关键词： Construction industry

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Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

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Neuroscience Bulletin 2020年第12期36卷 1454-1473页

作者： Bin He Long Cao Xiaoluan Xia Baogui Zhang Dan Zhang Bo You Lingzhong Fan Tianzi Jiang School of Mechanical and Power Engineering Harbin University of Science and TechnologyHarbin 150080China Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of Sciences(CAS)Beijing 100190China Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationCASBeijing 100190China Key Laboratory for Neuroinformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China The Queensland Brain Institute University of QueenslandBrisbaneQLD 4072Australia University of CAS Beijing 100049China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030600China Chinese Institute for Brain Research Beijing 102206China Core Facility Center of Biomedical AnalysisTsinghua UniversityBeijing 100084China

The frontal pole cortex(FPC)plays key roles in various higher-order functions and is highly developed in non-human *** essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer *** is important for understanding the functional architecture of the cerebral ***,combining cross-validation and principal component analysis,we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC(2 males and 6 females)into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4 T Bruker system,and then revealed their subregional ***,we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions,and found that the dorsolateral FPC,which contains an extension to the medial FPC,was mainly connected to regions of the default-mode *** ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive *** results enhance our understanding of the anatomy and circuitry of the macaque brain,and contribute to FPC-related clinical research.

关键词： Macaque Frontal pole cortex Anatomical connectivity profile Parcellation Neuroimaging Principal component analysis

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Ambient Brightness Issues in AR-BCI Applications: Phenomenon and Solutions

TechRxiv

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TechRxiv 2022年

作者： Zhang, Rui Yao, Dezhong Cao, Lijun Xu, Zongxin Zhang, Yangsong Zhang, Lipeng Hu, Yuxia Chen, Mingming Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu611731 China

Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. In addition, we designed a new SSVEP recognition algorithm, named ensemble online adaptive CCA (eOACCA), to solve the accuracy loss problem in high ambient brightness. The main strategy of eOACCA is to provide initial filters for high-intensity data by iteratively learning low-light-intensity SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities (>600 lux). Compared with FBCCA, the accuracies of eOACCA under 1200 lux was increased by 12.83%. In conclusion, current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful to promote the AR-BCI application in complex lighting environments. © 2022-11-30, CC BY.

关键词： Augmented reality

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Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior

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Psychoradiology 2021年第1期1卷 23-41页

作者： Xi Jiang Tuo Zhang Shu Zhang Keith MKendrick Tianming Liu School of Life Science and Technology MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation Northwestern Polytechnical UniversityXi’an 710129China Center for Brain and Brain-Inspired Computing Research School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China Cortical Architecture Imaging and Discovery Laboratory Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGA 30605USA

Folding of the cerebral cortex is a prominent characteristic of mammalian *** or deficits in cortical folding are strongly correlated with abnormal brain function,cognition,and ***,a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and *** and sulci,the standard nomenclature for cortical anatomy,serve as building blocks to make up complex folding patterns,providing a window to decipher cortical anatomy and its relation with brain *** efforts have been devoted to this research topic from a variety of disciplines including genetics,cell biology,anatomy,neuroimaging,and neurology,as well as involving computational approaches based on machine learning and artificial intelligence ***,despite increasing progress,our understanding of the functional anatomy of gyro-sulcal patterns is still in its *** this review,we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci,as well as the supporting information from genetic,cell biology,and brain structure *** particular,we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and ***,this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function,cognition,and behavior,as well as to mental disorders.

关键词： cortical folding gyro-sulcal pattern brain anatomo-function

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brain-computer interface for stroke rehabilitation with clinical studies

Brain-computer interface for stroke rehabilitation with clin...

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International Winter Workshop on brain-computer interface (BCI)

作者： Cuntai Guan Brain-Computer Interface Laboratory Institute for Infocomm Research Agency for Science Technology and Research Singapore

Summary form only given. Stroke is the leading cause of severe disabilities in the developed world. Each year, there are around 15 million new stroke cases worldwide. About 30% of stroke survivors need various forms of rehabilitation. Among these, upper limb weakness and loss of hand function are among the most devastating types of disabilities. Despite optimal acute medical treatment and modern rehabilitation, 45% of the patients do not achieve complete recovery of their bodily functions. In addition, 85% to 90% of stroke survivors with upper limb impairment do not regain full functional use of their upper extremities. Limitations in current physiotherapy and occupational therapy techniques include: (i) difficulties in rehabilitation for the severely paralyzed arm and hand which are often treated with passive modalities, (ii) difficulties in achieving intensive rehabilitation and high repetitions in those with moderate to severe upper extremity paralysis, (iii) problems in motivating and sustaining patient interest in repetitive exercises, (iv) therapy is often perceived to be boring due to lack of immediate biofeedback.

关键词： brain-computer interfaces DC motors Medical treatment Robots Electroencephalography Conferences Motor drives

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Heterogeneous defect prediction via exploiting correlation subspace 28

Heterogeneous defect prediction via exploiting correlation s...

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28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016

作者： Cheng, Ming Wu, Guoqing Jiang, Min Wan, Hongyan You, Guoan Yuan, Mengting State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Department of Cognitive Science and Technology Fujian Key Laboratory of Brain-Like Intelligent Systems Xiamen University Xiamen361005 China

ISBN: (纸本)189170639X

Software defect prediction generally builds models from intra-project data. Lack of training data at the early stage of software testing limits the efficiency of prediction in practice. Thereby researchers proposed cross-project defect prediction using the data from other projects. Most previous efforts assumed the cross-project defect data have the same metrics set which means the metrics used and size of metrics set are same in the data of projects. However, in real scenarios, this assumption may not hold. In addition, software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis for deriving a joint feature space for associating crossproject data and propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. Experiments on public heterogeneous datasets from different projects show that our method is more effective, compared to state-of-the-art methods. Copyright © 2016 by KSI Research Inc. and Knowledge Systems Institute Graduate School.

关键词： Support vector machines

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Quantum Mechanical Tunnelling Probes with Redox Cycling for Ultra-Sensitive Detection of Biomolecules

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Angewandte Chemie 2025年

作者： Long Yi Tao Jiang Dr. Ren Ren Prof. Ji Cao Prof. Joshua B. Edel Prof. Aleksandar P. Ivanov Prof. Longhua Tang State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Nanhu Brain-Computer Interface Institute Second Affiliated Hospital School of Medicine Hangzhou Zhejiang 311100 China Department of Chemistry Molecular Science Research Hub Imperial College London White City Campus 82 Wood Lane London W12 0BZ UK Institute of Pharmacology and Toxicology Zhejiang Province Key Laboratory of Anti-Cancer Drug Research College of Pharmaceutical Sciences Zhejiang University Hangzhou China

Quantum mechanical tunnelling sensors (QMTs) have emerged as a promising technology for next-generation single-molecule detection. Furthermore, QMT sensors can be combined with redox species resulting in repeated oxidation and reduction (redox cycling).. We developed robust QMT probes with electrode gap distances below 2 nm. Using the generator-collector (GC) mode, we verified that redox cycling of the ferrocyanide/ferricyanide (Fe(CN) 6 3−/4− ) couple occurs both in the tunnelling regime and on the electrode surface. Our findings indicated that the current enhancement is affected by both the gap distance and surface modifications of the probes. These QMT probes exhibited remarkable sensitivity, capable of detecting Fe(CN) 6 3−/4− concentrations down to sub-picomolar levels. Utilising this ability to modulate redox reactions, we adapted the QMT probes to serve as electrochemical sensors for detecting viral proteins. By modifying the electrode surfaces, our functionalised QMT probes achieved sub-pM detection limits with high selectivity in biofluids such as nasopharyngeal secretions. These findings highlight the potential of QMT probes to develop into a new class of electrochemical tunnelling sensors, offering significant advancements in biomedical diagnostics.

关键词： quantum tunnelling sensors redox cycling reaction single-molecular detection biosensor

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