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arXiv 2022年

作者： Chen, Jianbo Zhang, Yangsong Pan, Yudong Xu, Peng Guan, Cuntai Laboratory for Brain Science and Medical Artificial Intelligence School of Computer Science and Technology Southwest University of Science and Technology Mianyang China MOE Key Laboratory for NeuroInformation Clinical Hospital of Chengdu Brain Science Institute Center for Information in BioMedicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Nanyang Technological University Singapore

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data become urgent. In recent years, developing the methods that can work in inter-subject classification scenario has become a promising new direction. As the popular deep learning model nowadays, Transformer has excellent performance and has been used in EEG signal classification tasks. Therefore, in this study, we propose a deep learning model for SSVEP classification based on Transformer structure in inter-subject classification scenario, termed as SSVEPformer, which is the first application of the transformer to the classification of SSVEP. Inspired by previous studies, the model adopts the frequency spectrum of SSVEP data as input, and explores the spectral and spatial domain information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) is proposed to further improve the classification performance. Experiments were conducted using two open datasets © 2022, CC BY.

关键词： Classification (of information)

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Hydrogel wound dressings containing bioactive compounds originated from traditional Chinese herbs: A review

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Smart Materials in Medicine 2024年第1期5卷 153-165页

作者： Yang, Dan Chen, Hailan Wei, Hua Liu, An Wei, Dai-Xu Chen, Jing Institute of Medical Sciences The Second Hospital and Shandong University Center for Orthopaedics Cheeloo College of Medicine Shandong University Jinan250033 China Faculty of Animal Science and Technology Guangxi University Guangxi Nanning530004 China Key Laboratory of Colloid and Interface Chemistry Ministry of Education School of Chemistry and Chemical Engineering Shandong University Jinan250100 China Department of Orthopedics The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou310002 China Zigong Affiliated Hospital of Southwest Medical University Zigong Psychiatric Research Center Zigong Institute of Brain Science Zigong Sichuan Province 643002 China Key Laboratory of Resource Biology and Biotechnology in Western China Ministry of Education School of Medicine Faculty of Life Science & Medicine Northwest University Xi'an710069 China

Various factors can cause skin defects, resulting in the loss of physiological functions and even death due to severe concurrent infection. Dressings are often clinically used to fully cover the wounds to improve healing. Hydrogel wound dressings can be loaded with therapeutic compounds (e.g., curcumin) within their three-dimensional networks to enable the in situ delivery of compounds at skin defects for wound healing. In recent decades, natural herbal active components have gradually gained worldwide recognition owing to their safe and diverse therapeutic effects, and an increasing number of bioactive components can be loaded into hydrogels or directly act as hydrogel matrices to enhance safety and achieve the desired therapeutic effects. In this review, twelve bioactive compounds from natural Chinese herbs that can promote wound healing and their mechanism of action are summarized, and the latest research progress in the use of Chinese herbal hydrogels for wound treatment is reviewed. © 2023 The Authors

关键词： Hydrogels

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

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

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IEEE Symposium on Computational Intelligence and Games, CIG

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

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.

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Sample Set Reduction Method Based on Neighborhood Non-dominated Crowding-distance Sorting

Sample Set Reduction Method Based on Neighborhood Non-domina...

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International Symposium on Computational Intelligence and Design

作者： Mengmeng Li Zhigang Shang Xiaoyang Shen Caitong Yue Haofeng Wang Yonghui Dong Hong Wan Industrial Technology Research Institute Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China School of Electrical Engineering Zhengzhou University Zhengzhou China

To remove the redundant samples and reduce sample set size of big data analysis while ensuring classification accuracy, a sample set reduction method based on Neighborhood Non-dominated Crowding-distance Sorting (NNCS) is proposed. The proposed method is based on the idea of neighborhood, in which the distance between the samples of one class and the other classes is taken as the evaluation criterion. Furthermore, optimization algorithm NSGA-II is adapted to select the key samples by non-dominated crowding-distance sorting and the purpose of reducing the sample set is achieved finally. The experimental results on nine UCI standard data sets show that the method has good generalization performance and reduces the sample set size effectively under the premise of fully guaranteed classification accuracy.

关键词： Sample set reduction key sample Neighborhood Non-dominated sorting Crowding-distance sorting

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Adaptive initialization method for K-means algorithm

arXiv

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arXiv 2019年

作者： Yang, Jie Wang, Yu-Kai Yao, Xin Lin, Chin-Teng Computational Intelligence and Brain Computer Interface Lab Centre for AI FEIT University of Technology Sydney Australia Shenzhen Key Laboratory of Computational Intelligence Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CERCIA School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initialization methods have been proposed, but none of them can dynamically adapt to datasets with various characteristics. In our previous research, an initialization method for K-means based on hybrid distance was proposed, and this algorithm can adapt to datasets with different characteristics. However, it has the following drawbacks: (a) When calculating density, the threshold cannot be uniquely determined, resulting in unstable results. (b) Heavily depending on adjusting the parameter, the parameter must be adjusted five times to obtain better clustering results. (c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets. In the current paper, we proposed an adaptive initialization method for the K-means algorithm (AIMK) to improve our previous work. AIMK can not only adapt to datasets with various characteristics but also obtain better clustering results within two interactions. In addition, we then leverage random sampling in AIMK, which is named as AIMK-RS, to reduce the time complexity. AIMK-RS is easily applied to large and high-dimensional datasets. We compared AIMK and AIMK-RS with 10 different algorithms on 16 normal and six extra-large datasets. The experimental results show that AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Furthermore, AIMK-RS can significantly reduce the complexity of applying it to extra-large datasets with high dimensions. The time complexity of AIMK-RS is O(n). Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

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

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

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Rehabilitation Engineering and Assistive technology (i-CREATe), International Convention on

作者： Yiwen Zhang Wenzhi Wang Gan Liu Yi Qiao Ruochuan Shi Yahan Duan Ying Du Sining Li Fengrui Guo Thomas John McHugh Feng Duan Tianjin key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin P. R. China Laboratory for Circuit and Behavioral Physiology RIKEN Center for Brain Science Wakoshi Saitama Japan Department of Life Sciences Graduate School of Arts and Sciences The University of Tokyo Tokyo Japan

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

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.

关键词： Deep learning Accuracy Robot control Pattern classification Human-robot interaction Assistive robots Motors Electroencephalography brain-computer interfaces Classification algorithms

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Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs

Learning across multi-stimulus enhances target recognition m...

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作者： Wong, Chi Man Wan, Feng Wang, Boyu Wang, Ze Nan, Wenya Lao, Ka Fai Mak, Peng Un Vai, Mang I. Rosa, Agostinho Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China Centre for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau China Department of Computer Science University of Western Ontario LondonON Canada Department of Psychology Shanghai Normal University Shanghai China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China LaSEEB-ISR-LARSyS Universidade de Lisboa Lisbon Portugal

Objective. Latest target recognition methods that are equipped with learning from the subject's calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. Approach. A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. Main results. Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. Significance. A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs. © 2020 IOP Publishing Ltd.

关键词： brain computer interface

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Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation

arXiv

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arXiv 2024年

作者： Zhang, Qingquan Li, Yuchen Lin, Yuhang Wang, Handing Liu, Jialin Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Artificial Intelligence Xidian University Xi’an China

This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multiobjective evolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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The influence of physiological environment on the targeting effect of aptamer-guided gold nanoparticles

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Nano Research 2019年第1期12卷 129-135页

作者： Ding Ding Yinling Zhang Edward A.Sykes Long Chen Zhuo Chen Weihong Tan Molecular Science and Biomedicine Laboratory(MBL) State Key Laboratory of Chemo/Bio-Sensing and ChemometricsCollege of Chemistry and Chemical Engineering and College of Life SciencesHunan UniversityChangsha 410082China School of Medicine Queens University81 Barrie StreetKingstonONK7L 3N6Canada Faculty of Science and Technology University of MacaoAvenida da UniversidadeMacao Taipa 999078China Department of Chemistry Departments of Physiology and Functional GenomicsCenter far Research at the Bio/Nano InterfaceUF Health Cancer CenterUF Genetics Institute and McKnight Brain InstituteUniversity of FloridaGainesvilleFlorida 32611USA Institute of Molecular Medicine Renji HospitalShanghai Jiao Tong University School of MedicineShanghai Jiao Tong UniversityShanghai 200240China

Aptamer guided nano medicine shows great promise in targeted cancer *** the loss of targeting capacity duri ng in vivo or clinical trials has largely hindered its popularity and there are no systematic studies to elucidate the ***,we investigated such loss of targeting capacity by examining how the physiological milieu affected targeting *** functionalized gold nanoparticle(AuNP)was chosen as the model and exposed to human blood serum that is used to mimic physiological *** light scattering(DLS),flow cytometry and label-free liquid chromatography tan dem mass spectrometry(LC-MS/MS)were employed to determi ne variations of NPs'surface chemistry and biological identities changes after serum *** showed that the targeting ability loss was caused by protein corona blocking,replacement and enzymatic cleavage of surface aptamer targeting ***,the aggregation issue is critical for the smaller *** of the protein corona profile indicated the accumulation of immune-related proteins on the surface of aptamer-conjugated NPs,which could induce immune response,resulting in rapid clearanee of NPs.

关键词： targeting effect aptamer nanomedicine physiological environment

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Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation

Interpreting Multi-objective Evolutionary Algorithms via Sok...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Qingquan Zhang Yuchen Li Yuhang Lin Handing Wang Jialin Liu Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China School of Artificial Intelligence Xidian University Xi’an China

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial diversity of Sokoban levels, we illustrate the improved two-archive algorithm, Two_Arch2, a well-known multi-objective evolutionary algorithm. Our web-based platform integrates Two_Arch2 into an interface that visually and interactively demonstrates the evolutionary process in real-time. Designed to bridge theoretical optimisation strategies with practical game generation applications, the interface is also accessible to both researchers and beginners to multi-objective evolutionary algorithms or procedural content generation on a website. Through dynamic visualisations and interactive gameplay demonstrations, this web-based platform also has potential as an educational tool.

关键词： Bridges Visualization Procedural generation Spatial diversity Evolutionary computation Real-time systems Optimization

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