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Chinese Automation Congress (CAC)

作者： Mengmeng Li Jianbo Zhang Hong Wan Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Research has shown that practicing Tai Chi can alleviate brain tension and enhance the activity of the autonomic nervous system in the human body. However, there is limited research on the effects of Tai Chi Stance on brain function. This study aims to analyze the electroencephalogram (EEG) characteristics of long-term Tai Chi Zhuang practitioners and investigate the changes in brain dynamic neural patterns during different stages of Tai Chi Stance, together with the differences between long-term practitioners and a control group. In this study, we first analyzed the changes in EEG spectral characteristics during different stages of Tai Chi Stance practice of long-term practitioners. We found that the alpha (8–13 Hz) power significantly increased during the stance, indicating a state of deep relaxation during the Tai Chi practice. The experimental group also exhibited significant changes in brain functional network parameters in the alpha band during the stance, including increased clustering coefficient and global efficiency. Finally, inter-group differences in EEG characteristic parameters were analyzed between the experimental group and control group. The results showed significant differences in relative power and functional connectivity in the alpha band between the two groups, indicating that long-term Tai Chi Stance practice can effectively enhance brain network efficiency and improve information integration capabilities. This study provides scientific evidence for Tai Chi Stance training to enhance brain health.

关键词： Training Automation Autonomic nervous system Focusing Electroencephalography

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A Hierarchical Intelligent Rehabilitation Robotic System Based on MI-EEG

A Hierarchical Intelligent Rehabilitation Robotic System Bas...

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第43届中国控制会议

作者： Yi Qiao Ruochuan Shi Fengrui Guo Bo Zhao Wenyu Li Zhongxin Liu Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin First Central Hospital

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In 2022,global stroke cases reached 101 million,posing a significant health *** rehabilitation methods are primarily passive,while active recovery therapy leveraging brain plasticity proves more ***-computer interfaces(BCIs) establish a pathway between individuals and external *** address this,we have developed a robotic system that combines a BCI system with active *** system consists of two components:signals acquisition and classification,and robot *** electroencephalography(EEG) signals,we use the Filter Bank Common Spatial Pattern(FBCSP)algorithm to extract spatial-frequency features and employ machine learning methods for ***,we enhance classification effectiveness using *** robotic system,divided into navigation,vision,and robotic arm modules,simulates patient-robot interaction through motor imagery(MI) *** on five participants demonstrate EEGNet's superior classification accuracy(81.75% vs.75.45%),with an overall task completion accuracy of 70.91%.The results show that the robotic system has a high accuracy in classifying MI-EEG,which is expected to create an intelligent rehabilitation system for future stroke patients.

关键词： brain-computer interface motor imagery human-machine interaction rehabilitation robot

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DSCT:a novel deep-learning framework for rapid and accurate spatial transcriptomic cell typing

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National Science Review 2025年第5期12卷 123-138页

作者： Yiheng Xu Bin Yu Xuan Chen Aibing Peng Quyuan Tao Youzhe He Yueming Wang Xiao-Ming Li Department of Neurology and Department of Psychiatry the Second Affiliated HospitalZhejiang University School of Medicine NHC and CAMS Key Laboratory of Medical Neurobiology MOE Frontier Center of Brain Science and Brain-machine Integration School of Brain Science and Brain MedicineZhejiang University College of Computer Science and Technology Zhejiang University Key Laboratory of Novel Targets and Drug Study for Neural Repair of Zhejiang Province Institute of brain and cognitive science Hangzhou City University School of Medicine Department of Neurobiology Affiliated Mental Health Center & Hangzhou Seventh People's HospitalZhejiang University School of Medicine BGI Research Qiushi Academy for Advanced Studies Zhejiang University The Nanhu Brain-computer Interface institute

Unraveling complex cell-type-composition and gene-expression patterns at the cellular spatial resolution is crucial for understanding intricate cell functions in the brain. In this study, we developed Deep neural Network-based Spatial Cell Typing(DSCT)—an innovative framework for spatial cell typing within spatial transcriptomic data sets. This approach utilizes a synergistic integration of an enhanced gene-selection strategy and a lightweight deep neural network for data training, offering a more rapid and accurate solution for the analysis of spatial transcriptomic data. Based on comprehensive analysis, DSCT achieved exceptional accuracy in cell-type identification across various brain regions, species and spatial transcriptomic *** also performed well in mapping finer cell types, thereby showcasing its versatility and adaptability across diverse data sets. Strikingly, DSCT exhibited high efficiency and remarkable processing speed, with fewer computational resource demands. As such, this novel approach opens new avenues for exploring the spatial organization of cell types and gene-expression patterns, advancing our understanding of biological functions and pathologies within the nervous system.

关键词： DSCT spatial cell typing spatial transcriptomics deep-learning neural network

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sEMG-Based Gesture Recognition in Multiple Scenarios Using a Phase Locked Value-Based Deep Learning Method

sEMG-Based Gesture Recognition in Multiple Scenarios Using a...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xueze Zhang Minjie Wang Zihua Chen Lu Wang Hongbo Wang Xiaoyang Kang MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Institude of Meta-Medical Academy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for sEMG-based gesture recognition. We conducted validation experiments on public datasets by integrating the previous proposed gesture recognition algorithms, STCN-GR and ConSSL. PLV-STCN-GR and PLV-ConSSL were utilized to test the performance of proposed feature extraction method in intra-session, inter-session and inter-subject scenarios. In general, the proposed PLV-based sEMG decoding framework has achieved good recognition results on public datasets and demonstrated the possibility of its application in practical scenarios.

关键词： Human computer interaction Deep learning Gesture recognition Feature extraction Electromyography Decoding Bioinformatics

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Rats Behavior Recognition Based on Improved YOLOv8

Rats Behavior Recognition Based on Improved YOLOv8

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IEEE International Conference on Robotics and Biomimetics

作者： Zhuoxuan Bai Yifan Li Zhihao Ru Sining Li Wenyu Li Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation College of Artificial Intelligence Nankai University Tianjin

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In the domains of neuroscience and pharmacology, rodent models are crucial for deepening our understanding of neurobiological processes. Traditional behavioral recognition methods, however, encounter issues such as high costs and suboptimal accuracy. This research presents a pioneering method that employs Deformable Attention to enhance rat behavior recognition on an observation platform. By integrating Deformable Convolutional Networks (DCNs) into the YOLOv8 algorithm, the approach amplifies detection precision and robustness. We performed experiments utilizing six hours of video data from rats navigating an eight-arm maze, categorizing behaviors including walking, sniffing, barbering, eating and so on. The refined model showed a substantial increase in mean Average Precision (mAP), from 0.776 to 0.990, along with a smoother and more stable loss curve, indicating enhanced predictive performance. This study demonstrates the feasibility of leveraging standard cameras and sophisticated machine learning techniques for effective and precise behavior analysis under limited resource conditions.

关键词： Neuroscience Navigation Robot vision systems Rodents Rats Predictive models Robustness Pharmacology Standards Robots

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Chinese patients with adult onset leukodystrophy caused by CST3 variants

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Journal of Genetics and Genomics 2024年第7期51卷 778-780页

作者： Yi Zhang Zhi-Ying Wu Department of Medical Genetics and Center for Rare Diseases Zhejiang Key Laboratory of Rare Diseases for Precision Medicine and Clinical TranslationSecond Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang 310009China Nanhu Brain-computer Interface Institute HangzhouZhejiang 314050China Department of Neurology Second Affiliated HospitalZhejiang University School of MedicineHangzhouZhejiang 310009China MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain MedicineZhejiang UniversityHangzhouZhejiang 310012China

Leukodystrophies represent a group of cerebral white matter disorders that mainly affect axon-glia *** disorders are clinically diverse and display significant genetic *** is challenging to differentiate hereditary leukodystrophies,particularly those that present in adulthood,from acquired leukodystrophies and other genetic disorders,such as multiple sclerosis(MS)or hereditary spastic paraplegia(HSP)(Wei et al.,2021).Over the past few decades,a series of causative genes associated with leukodystrophies have been identified(Kohler et al.,2018).Nevertheless,a significant proportion of patients still lack a precise molecular diagnosis.

关键词： al. patients Kohler

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Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation 39

Self-Attentive Spatio-Temporal Calibration for Precise Inter...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hong, Di Wang, Yueming The College of Computer Science and Technology Zhejiang University China Nanhu Brain-computer Interface Institute Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University China

ISBN: (纸本)157735897X

Spiking neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial neural Networks (ANNs). ANN-to-SNN knowledge distillation can improve SNN performance, but previous methods either focus solely on label information, missing valuable intermediate layer features, or use a layer-wise approach that neglects spatial and temporal semantic inconsistencies, leading to performance degradation. To address these limitations, we propose a novel method called self-attentive spatio-temporal calibration (SASTC). SASTC uses self-attention to identify semantically aligned layer pairs between ANN and SNN, both spatially and temporally. This enables the autonomous transfer of relevant semantic information. Extensive experiments show that SASTC outperforms existing methods, effectively solving the mismatching problem. Superior accuracy results include 95.12% on CIFAR-10, 79.40% on CIFAR-100 with 2 time steps, and 68.69% on ImageNet with 4 time steps for static datasets, and 97.92% on DVS-Gesture and 83.60% on DVS-CIFAR10 for neuromorphic datasets. This marks the first time SNNs have outperformed ANNs on both CIFAR-10 and CIFAR-100, shedding the new light on the potential applications of SNNs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Structures and ion transport mechanisms of plant high-affinity potassium transporters

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Molecular Plant 2024年第3期17卷 409-422页

作者： Jiangqin Wang Yanping Luo Fan Ye Zhong Jie Ding Shao Jian Zheng Shuai Qiao Yong Wang Jiangtao Guo Wei Yang Nannan Su international Institutes of Medicine the Fourth Affiliated HospitalZhejang University School of MedicineYiwuZhejiang 322000China Department of Biophysics and Department of Neurology of the Fourth Affiliated Hospital Zhejiang University School of MedicineHangzhouZhejang 310058China State Key Laboratory of Plant Environmental Resilience College of Life SciencesZhejang UniversityHangzhouZhejang 310058China College of Life Sciences Zhejiang UniversityHangzhouZhejiang 310058China NHC and CAMS Key Laboratory of Medical Neurobiology MOE Frontier Science Center for Brain Science and Brain-machine IntegrationSchool of Brain Science and Brain MedicineZhejiang UniversityHangzhouZhejiang 310058China Nanhu Brain-computer Interface Institute Hangzhou.Zheiang 311100.China

Plant high-affinity K^(+) transporters(HKTs)mediate Na^(+) and K^(+) uptake,maintain Na^(+)/K^(+) homeostasis,and therefore play crucial roles in plant salt *** this study,we present cryoelectron microscopy structures of HKTs from two classes,classI HKT1;1 from Arabidopsis thaliana(AtHKT1;1)and class II HKT2;1 from Triticum aestivum(TaHKT2;1),in both Na^(+) -and K^(+) -bound states at 2.6-to 3.0-A ***1;1and TaHKT2;1function *** HKT subunit consists of four tan-dem domain units(D1-D4)with a repeated K^(+) -channel-like M-P-M *** each subunit,D1-D4 assemble into an ion conduction pore with a pseudo-four-fold *** both TaHKT2;1and AtHKT1;1 have only one putative Na^(+) ion bound in the selectivity filter with a similar coordination pattern,the two HKTs display different K^(+) binding modes in the ***2;1 has three K^(+) ions bound in the selec-tivity filter,but AtHKT1;1 has only two K^(+) ions bound in the filter,which has a narrowed external entrance due to the presence of a Ser residue in the first filter *** structures,along with compu-tational,mutational,and electrophysiological analyses,enable us to pinpoint key residues that are critical for the ion selectivity of *** findings provide new insights into the ion selectivity and ion transport mechanisms of plant HKTs and improve our understanding about how HKTs mediate plant salt tolerance and enhance crop growth.

关键词： HKTs structure ion selectivity mechanism salt tolerance

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Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation

arXiv

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arXiv 2025年

关键词： Spatio-temporal data

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Motion Sickness Alleviation Based on a Mindfulness brain-computer interface

Motion Sickness Alleviation Based on a Mindfulness Brain-Com...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Jiawei Zhu Xiaoyu Bao Qiyun Huang Tao Wang Li Huang Yupeng Han Kendi Li Di Chen Kailin Xu Zijian Wang Ya Jiang Yuanqing Li Dept. School of Automation Science and Engineering South China University of Technology Dept. Research Center for Brain–Computer Interface Pazhou Laboratory Guangzhou China Huizhou Desay SV Automotive Co. Ltd. Huizhou China Dept. Research Center for Brain–Computer Interface Pazhou Laboratory South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

Motion sickness is a common affliction that affects nearly half of the global population and poses challenges to comfortable travel experiences, necessitating diverse intervention strategies. Pharmacological interventions have shown promise but present limitations due to side effects. To date, there is no non- pharmacological alleviation method for motion sickness that has been proven effective in a large population. In this study, we developed a novel mindfulness brain-computer interface (mindfulness BCI) for alleviation of motion sickness. Thirty-one subjects who were susceptible to motion sickness participated in our experiments in a real-world car motion environment. The experimental results demonstrated that the proposed mindfulness BCI can effectively alleviate motion sickness symptoms, where more than 90% of subjects reported the effectiveness of the mindfulness BCI system and a significant reduction of Motion Sickness Susceptibility Questionnaire (MISC) scores was obtained when comparing the BCI-based meditation state with the resting state for the subjects (t 30 =4.968, P<0.001). To the best of our knowledge, our mindfulness BCI is the first effective non- pharmacological alleviation method for motion sickness as demonstrated by a strict test.

关键词： Training Sociology Motion sickness Motors brain-computer interfaces Automobiles Statistics

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