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2024 China Automation Congress, CAC 2024

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

ISBN: (纸本)9798350368604

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. © 2024 IEEE.

关键词： Electroencephalography

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Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

arXiv

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arXiv 2025年

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao School of Artificial Intelligence Anhui University Hefei230601 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetoencephalography

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Consistent brain Age Difference in Childhood Autism Spectrum Disorder and its Subtypes

Consistent Brain Age Difference in Childhood Autism Spectrum...

引用

4th International Workshop on Human brain and Artificial Intelligence, HBAI 2024

作者： Sun, Fangling Liang, Chuang Shao, Wei Fu, Zening Zhang, Daoqiang Jiang, Rongtao Qi, Shile Calhoun, Vince D. Department of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Georgia State University Institute of Technology Emory University AtlantaGA United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States

ISBN: (纸本)9789819640003

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impairments in social interaction and behavior as well as structural abnormalities regarding brain development. The difference between neuroimaging-predicted brain age and the chronological age (predicted age difference;PAD) is a potential biomarker reflecting individual differences in brain developmental trajectories. However, current findings of PAD in ASD are inconsistent due to the biases introduced by different prediction models. Here, 6 classic 3-dimensional convolutional neural network (3D-CNN) models were used to assess brain age to identify consistent and reliable PAD and its interpretable features for ASD. No significant PAD differences between ASD and typically developing controls (TDC) were observed, but PAD differences in younger age and subtypes were found. Occlusion sensitivity analysis showed that default mode network (DMN) and salience network (SAN) drove the atypical brain developmental trajectories in ASD. Our findings were replicable across 6 CNN models, showing the promise of neuroimaging targets for exploring atypical developmental patterns in ASD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Corrections to “brain-Inspired Computing: A Systematic Survey and Future Trends”

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Proceedings of the IEEE 2025年第12期112卷 1850-1850页

作者： Guoqi Li Lei Deng Huajin Tang Gang Pan Yonghong Tian Kaushik Roy Wolfgang Maass Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China Department of Precision Instrument Tsinghua University Beijing China State Key Laboratory of Brain-Machine Intelligence College of Computer Science and Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China Department of Computer Science Peking University Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette IN USA School of Computer Science Graz University of Technology Graz Austria

Presents corrections to the paper, (Corrections to “brain-Inspired Computing: A Systematic Survey and Future Trends”).

关键词： Artificial intelligence Computational modeling brain modeling Biological system modeling Surveys computer architecture Benchmark testing Bio-inspired computing Neuromorphic engineering Sensors Neural networks Software tools

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Optimal Distribution of Solutions for Crowding Distance on Linear Pareto Fronts of Two-Objective Optimization Problems

arXiv

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arXiv 2025年

作者： Ishibuchi, Hisao Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Characteristics of an evolutionary multi-objective optimization (EMO) algorithm can be explained using its best solution set. For example, the best solution set for SMS-EMOA is the same as the optimal distribution of solutions for hypervolume maximization. For NSGA-III, if the Pareto front has intersection points with all reference lines, all of those intersection points are the best solution set. For MOEA/D, the best solution set is the set of the optimal solution of each sub-problem. Whereas these EMO algorithms can be analyzed in this manner, the best solution set for the most well-known and frequently-used EMO algorithm NSGA-II has not been discussed in the literature. This is because NSGA-II is not based on any clear criterion to be optimized (e.g., hypervolume maximization, distance minimization to the nearest reference line). As the first step toward the best solution set analysis for NSGA-II, we discuss the optimal distribution of solutions for the crowding distance under the simplest setting: the maximization of the minimum crowding distance on linear Pareto fronts of two-objective optimization problems. That is, we discuss the optimal distribution of solutions on a straight line. Our theoretical analysis shows that the uniformly distributed solutions are not the best solution set. However, it is also shown by computational experiments that the uniformly distributed solutions (except for the duplicated two extreme solutions at each edge of the Pareto front) are obtained from a modified NSGA-II with the (μ + 1) generation update scheme. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Concurrent TMS-EEG to characterize cortical responses in the motor and prefrontal cortices in Parkinson's disease

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Neurotherapeutics 2025年 e00577页

作者： Zhu, Lin Cai, Min Pei, Zian Shi, Xue Dang, Ge Lan, Xiaoyong Luo, Xiaoguang Che, Xianwei Guo, Yi Department of Neurology Shenzhen People's Hospital (The Second Clinical Medical College Jinan University The First Affiliated Hospital Southern University of Science and Technology) Guangdong Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Shenzhen Bay Laboratory Guangdong Shenzhen China Centre for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China Henan Key Laboratory of Neurorestoratology Henan Joint International Research Laboratory of Neurorestoratology for Senile Dementia Department of Neurology The First Affiliated Hospital of Xinxiang Medical University Xinxiang China

Patients with Parkinson's disease (PD) experience both motor and non-motor symptoms. However, it remains unclear the full spectrum of PD, which requires a comprehensive assessment of both motor and non-motor cortical regions. The use of combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) allows the examination of neural circuit beyond motor cortex. In this study, TMS-EEG data were collected over the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) in 46 patients with PD and 27 healthy controls (HC). We analyzed TMS-evoked potentials and oscillatory powers to identify differences in cortical excitability and neural connectivity dynamics between the two cohorts. Patients with PD exhibited higher P30 amplitude following M1 stimulation compared to HCs, and there was a positive correlation between P30 amplitude and the severity of motor symptoms. DLPFC stimulation revealed an increased global mean field amplitude area under the curve (GMFA-AUC) at P30 and P60 in the PD group compared to HCs. A significant correlation was also observed within the PD group between P30 and P60 with depression scores. In addition, PD showed a significant power reduction in the alpha and beta bands during stimulation and distinct patterns emerged for each stimulation site. These findings provide novel insights into cortical network abnormalities and contribute to a better understanding of the mechanisms underlying PD. © 2025 The Authors

关键词： Electroencephalography (EEG) Motor and prefrontal cortex Oscillatory responses Parkinson's disease (PD) Transcranial magnetic stimulation (TMS)

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The Bird Flight Movement Information Acquisition Equipment Design and Implementation Based on The STM32

The Bird Flight Movement Information Acquisition Equipment D...

引用

2022 International Conference on computer, Artificial Intelligence, and Control Engineering, CAICE 2022

作者： Guo, Zhixin Yang, Long Ma, Erteng Li, Yang Shang, Zhigang School of Electrical Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9781510655942

Birds have excellent three-dimensional space movement ability. The research and design of small and lightweight bird flight movement behavior information collection equipment has important application value in the research of bird robots and other aspects. Based on STM32F103 series chips, this paper designs a low cost small lightweight bird flight movement information acquisition device, and uses FREERTOS multi-task real-time operating system to realize the acquisition and storage of movement behavior information on this device. © 2022 SPIE.

关键词： Birds

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Manipulating Piezoelectric and Electro-Strain Properties of BiFeO3-BaTiO3-Based Ceramics Through Chemical Doping-Controlled Domain-Size Engineering

SSRN

引用

SSRN 2025年

作者： Lin, Jiamin Liu, Bing Liu, Mengxiang Yang, Shan Zhou, Linming Hong, Zijian Zhu, Xiaoli Wu, Yongjun Li, Juan Huang, Yuhui School of Materials Science and Engineering State Key Laboratory of Silicon and Advanced Semiconductor Materials Cyrus Tang Center for Sensor Materials and Applications Institute of Fundamental and Transdisciplinary Research Zhejiang University Hangzhou310027 China Nanhu Brain-computer Interface Institute Zhejiang Hangzhou311100 China College of Electronic Information and Engineering Hangzhou Dianzi University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Taizhou Institute of Zhejiang University Zhejiang Taizhou318000 China School of Engineering Hangzhou City University Zhejiang Hangzhou310015 China College of Materials and Science Engineering Zhejiang University of Technology Zhejiang Hangzhou310006 China

Ferroelectric domains are crucial for the performance of piezoelectric ceramics, as the size and switching dynamics affect polarization response directly, manipulating both ferroelectric and piezoelectric properties. In this study, we achieved BiFeO3-BaTiO3 ceramics with domain sizes from macroscopic to nanoscale by incorporating a small amount of antiferroelectric phase. Contrary to the long-standing belief that smaller ferroelectric domains own lower polarization switching barriers and yield higher piezoelectricity, we found that in chemical doping-controlled domain size engineering, the smaller the domain size, the more difficult it is for polarization switching. Ceramics with the largest domain exhibited superior positive piezoelectric performance due to the sufficient polarization switching and reduced domain wall pinning, achieving a piezoelectric coefficient (d33) of 460 pC/N at 350°C. Middle-sized domains achieved optimal electro-strain performance determined by the trade-off between the domain walls pinning and the increased local tetragonal or orthorhombic symmetries, with a strain (0.29%) and an excellent piezoelectric strain coefficient (d33*) of 726.4 pm/V at 160°C. This work provides novel insights into how domain-size engineering affects domain switching and local distortion, offering guidance for performance optimization of piezoelectric ceramics. © 2025, The Authors. All rights reserved.

关键词： Piezoelectricity

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

引用

Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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