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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanyang Dong Shurong Sheng Xiongfei Wang Jia-Hong Gao Yi Sun Wanli Yang Kuntao Xiao Pengfei Teng Guoming Luan Zhao Lv School of Artificial Intelligence Anhui University Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei 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 Hefei China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

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.

关键词： Convolution Magnetic sensors Neural networks Magnetoencephalography Manuals Feature extraction Electroencephalography Encoding Recording Magnetic fields

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Shi, Qiuxia Hu, Bin Tian, Fuze Zhao, Qinglin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University China School of Computer and Communication Lanzhou University of Technology China Key Laboratory of Special Functional Materials and Structural Design Lanzhou University Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar. © 2022 IEEE.

关键词： Singular value decomposition

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Data-Driven Low-Carbon Smart Mobility Framework: A Case Study on Electric Vehicle Charging Services

SSRN

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SSRN 2024年

作者： Xie, Hongbin Zhang, Haoran Song, Xuan Shi, Zhuoran Song, Ge Peng, Jinquan Xie, Yi Wang, Zhongyi Shenzhen China Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Urban Planning and Design Peking University No.2199 Lishui Road Nanshan District Guangdong Shenzhen518055 China Huawei Technologies CO. Ltd. Shenzhen China

In order to further reduce environmental pollution, many countries around the world are advocating and implementing the policies of "carbon peaking" and "carbon neutralization". Low-carbon Smart Mobility (LSM) is an innovative and environmentally-friendly transportation development model proposed over the last few years. By effectively integrating transportation resources and providing comfortable and intelligent transportation services, LSM can improve people's acceptance and utilization of electric transportation and public transportation, so as to reduce greenhouse gas emissions. At present, the development of LSM is still in its infancy, and most researches on LSM focus on the integration at the service level, without sufficient effective collection and analysis of various traffic data generated by massive traffic equipments at the bottom layer, so there is a lack of traffic value-added services based on data analysis. To solve the problem above, we proposes a novel construction scheme of building a data-driven Low-carbon Smart Mobility system. The scheme mainly realizes the interconnection of traffic equipment and the collection of traffic data in the whole transportation system by introducing Internet of Things (IoT) technology. On the basis of this, it further realizes the analysis and decision-making of massive traffic data by using cutting-edge science and technology such as big data, artificial intelligence, large language models (LLM), cloud computing and blockchain, so as to provide users with more environmentally-friendly, intelligent, convenient and comfortable transportation services, accelerate the development of urban green transportation, and ultimately achieve the goals of "carbon peak" and "carbon neutralization" as soon as possible. At the end of this article, we conducted a numerical experiment that demonstrates a case of scheduling decision in data-driven management for an automotive charging battery supply chain, thereby illustrating the feas

关键词： Internet of things

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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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Multi-Track Music Generation with WGAN-GP and Attention Mechanisms

Multi-Track Music Generation with WGAN-GP and Attention Mech...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Luyu Chen Lin Shen Dan Yu Zhihua Wang Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Key Laboratory of Brain Health Intelligent Evaluation and Intervention Ministry of Education Beijing Institute of Technology Beijing China Educational Physiology Laboratory Graduate School of Education The University of Tokyo Tokyo Japan GLAM – the Group on Language Audio & Music Imperial College London London UK

Music generation with artificial intelligence is a complex and captivating task. The utilisation of generative adversarial networks (GANs) has exhibited promising outcomes in producing realistic and diverse music compositions. In this paper, we propose a model based on Wasserstein GAN with gradient penalty (WGAN-GP) for multi-track music generation. This model incorporates self-attention and introduces a novel cross-attention mechanism in the generator to enhance its expressive capability. Additionally, we transpose all music to C major in training to ensure data consistency and quality. Experimental results demonstrate that our model can produce multi-track music with enhanced rhythm and sound characteristics, accelerate convergence, and improve generation quality.

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Active Pointly-Supervised Instance Segmentation

arXiv

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arXiv 2022年

作者： Tang, Chufeng Xie, Lingxi Zhang, Gang Zhang, Xiaopeng Tian, Qi Hu, Xiaolin Department of Computer Science and Technology Institute for AI BNRist State Key Laboratory of Intelligent Technology and Systems Tsinghua University China Huawei Inc. China China IDG McGovern Institute for Brain Research Tsinghua University China

The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting, named active pointly-supervised instance segmentation (APIS), which starts with box-level annotations and iteratively samples a point within the box and asks if it falls on the object. The key of APIS is to find the most desirable points to maximize the segmentation accuracy with limited annotation budgets. We formulate this setting and propose several uncertainty-based sampling strategies. The model developed with these strategies yields consistent performance gain on the challenging MS-COCO dataset, compared against other learning strategies. The results suggest that APIS, integrating the advantages of active learning and point-based supervision, is an effective learning paradigm for label-efficient instance segmentation. Copyright © 2022, The Authors. All rights reserved.

关键词： Budget control

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Toward multi-target self-organizing pursuit in a partially observable Markov game

arXiv

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arXiv 2022年

作者： Sun, Lijun Chang, Yu-Cheng Lyu, Chao Shi, Ye Shi, Yuhui Lin, Chin-Teng Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Centre for Artificial Intelligence CIBCI Lab Faculty of Engineering and Information Technology University of Technology Sydney Australia College of Computer and Information Science Southwest University China School of Information Science and Technology ShanghaiTech University China

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve the implicit coordination capabilities in search and pursuit. We model a self-organizing system as a partially observable Markov game (POMG) featured by large-scale, decentralization, partial observation, and noncommunication. The proposed distributed algorithm-fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning (MARL) method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that by decomposing the SOP task, FSC2 achieves superior performance compared with other implicit coordination policies fully trained by general MARL algorithms. The scalability of FSC2 is proved that up to 2048 FSC2 agents perform efficient multi-target SOP with almost 100% capture rates. Empirical analyses and ablation studies verify the interpretability, rationality, and effectiveness of component algorithms in FSC2. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent agents

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CLDG: Contrastive Learning on Dynamic Graphs

arXiv

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arXiv 2024年

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Department of Computer Science and Technology Xi’an Jiaotong University China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China Department of Distance Education Xi’an Jiaotong University China School of Software Beihang University China Advanced Innovation Center for Big Data and Brain Computing Beihang University China

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph’s augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2024, CC BY.

关键词： Contrastive Learning

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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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