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Harmonized-Multinational qEEG norms (HarMNqEEG)

NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230601 China Cuban Center for Neurocience La Habana Cuba University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia The Cuban center aging longevity and healt

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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Hand Gesture Recognition Based on Multi-Classification Adaptive Neuro-Fuzzy Inference System and pMMG

Hand Gesture Recognition Based on Multi-Classification Adapt...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Lei Wang Jian Huang Dongrui Wu Tao Duan Rui Zong Shicong Jiang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Laboratory for Economics and Computation City University of Hong Kong Kowloon Hong Kong SAR China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

In this paper, a multi-classification adaptive neuro-fuzzy inference system combining neural-network and a TSK fuzzy system is proposed to recognize six commonly used gestures. Several techniques including mini-batch gradient descent with L2 regularization, DropRule and AdaBound are integrated to improve the generalization ability of the system and the efficiency of training. Numerical results show that the average classification accuracy of the multiple classifier systems is 95.12%, and this value is higher than some other multiple classifier systems (CNN, LDA, etc.) using MMG signals as the inputs for hand gesture recognition.

关键词： Training Electromyography Muscles Skin Robots Mechatronics Adaptive systems

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Enhanced Clutter Suppression and GMTIm Algorithm with Modified DKP and NCS for Single-Channel Spaceborne-Maneuvering BFSAR

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Yuzhou Song, Xuan Li, Yachao Wang, Yitao Guo, Yanhong Ye, Pei Wang, Xuanqi Shi, Guangming Xidian University National Key Laboratory of Radar Signal Processing Xi’an710071 China Northwestern Polytechnical University School of Astronautics Xi’an710072 China DalianNaval Academy Operation Software and Simulation Institute Dalian116018 China Dalian University of Technology Institute for Advanced Intelligence Dalian116024 China Xidian University School of Physics Xi’an710071 China Xidian University School of Artificial Intelligence Xi’an710071 China Peng Cheng Laboratory Shenzhen518055 China

Single-channel spaceborne-maneuvering bistatic forward-looking synthetic aperture radar (SS-BFSAR) enables the maneuvering platform to achieve high-resolution forward-looking imaging without deploying additional antennas or transmitting the radar signal. Nevertheless, achieving the suppression of spatial variant clutter with only the single-channel configuration remains a critical challenge for the ground moving target imaging (GMTIm) mission of SS-BFSAR. This study proposes an enhanced clutter suppression and GMTIm algorithm for SS-BFSAR. First, the proposed modified deramp-keystone processing (DKP) completely decouples the echo signal in range and azimuth while avoiding the signal-to-clutter ratio (SCR) degradation caused by the azimuth spectrum aliasing. Subsequently, two pre-focusing approaches are developed with nonlinear chirp scaling (NCS), i.e., global NCS and block NCS, to achieve deep focusing of spatial variant clutter while introducing differences of Doppler frequency position (DFP) between the clutter and GMT. These approaches preserve the clutter consistency of the pre-focus results, thereby ensuring that the GMT signal exhibits high SCR following the image domain cancellation. Finally, a matched filter and the proposed monostatic-equivalent model are used to refocus the GMT and estimate its velocity. The proposed algorithm can simultaneously obtain the well-focused ground scene image and the GMTIm result without DFP drift caused by the target’s motion. Comparative experiments using simulation and real data demonstrate the effectiveness and superiority of the proposed algorithm. © 1980-2012 IEEE.

关键词： Echo suppression

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Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

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Knowledge-Based Systems 2021年 217卷

作者： Zhang, Xiaocai Zheng, Yi Zhao, Zhixun Liu, Yuansheng Blumenstein, Michael Li, Jinyan Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia National Key Laboratory of Science and Technology on Blind Signal Processing ChengduSichuan610041 China College of Information Science and Engineering Hunan University ChangshaHunan410082 China Australian Artificial Intelligence Institute & Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates. © 2021

关键词： Trajectories

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3R: Word and Phoneme Edition based Data Augmentation for Lexical Punctuation Prediction

3R: Word and Phoneme Edition based Data Augmentation for Lex...

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International Conference on computational intelligence and Security

作者： Aihua Zheng Naipeng Ye Xiao Wang Xiao Song Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Hefei China Peking University Shenzhen Institute Shenzhen China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Existing Lexical Punctuation Prediction methods are mainly trained on the standard clean data while losing the generalization in practical automatic speech recognition (ASR) system with ubiquitous transcription errors. To bridge the gap between clean training data and noisy testing data, we propose three random (3R) data augmentation strategies: random word deletion (RWD), random word substitution (RWS), and random phoneme edition (RPE) in both word and phoneme levels on the training dataset. Specifically, we contribute an acoustically similar vocabulary with phoneme level editions for acoustically similar word substitution. In addition, we first introduce the RoBERTa-large model into a punctuation prediction task to capture the semantics and the long-distance dependencies in language. Extensive experiments on the English dataset IWSLT2011 yield to a new state-of-the-art comparing to the prevalent punctuation prediction methods.

关键词： Training Vocabulary Semantics Training data Predictive models Noise measurement Security

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MSCIAIG-Net: Multi-scale spatial-channel interactive attention and interpretable guidance for fine-grained ship classification and visual inference network in remote sensing images

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Engineering Applications of Artificial intelligence 2025年 157卷

作者： Chengqiang Zhao Shijie Chen Jiashu Zhang Sichuan Province Key Laboratory of Signal and Information Processing Southwest Jiaotong University Chengdu 611756 China School of Information Science and Technology Southwest Jiaotong University Chengdu 611756 China School of Engineering and Physics Science Heriot-Watt University Edinburgh EH144AS UK School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China

Fine-grained ship classification (FGSC) in remote sensing images (RSI) with high visual similarity is becoming increasingly important in civil and military applications involving ship monitoring. Existing studies predominantly seek to enhance the accuracy of classification and recognition of artificial intelligence models only by designing deep network structures or algorithms, which often extract redundant information containing much background noise, ultimately degrading the robustness of the fine-tuned model. We provide a multi-scale spatial-channel interactive attention and interpretable guidance network (MSCIAIG-Net) for FGSC in RSI. This is the first study to understand and address the issue of robustness degradation resulting from parameter redundancy in FGSC models for RSI through visualized attributions. Specifically, a multi-scale spatial channel interactive attention module (MSCIAM) is designed to model and interact with the importance between multi-scale spatial and channel features. Under the guidance of interpretability, a novel fine-grained classification loss with visual interpretation is developed, which strengthens the network's ability to learn discriminative feature representations in salient regions. Additionally, an interpretability-analysis-based network pruning (IANP) strategy is introduced to reduce the noise and redundant information from the network, thereby achieving model compression and performance enhancement within a certain range without sacrificing accuracy. Furthermore, performance verification is implemented on the public dataset, Fine-grained Ship Collection-23 (FGSC-23), and our collated dataset, and MSCIAIG-Net attains the competitive accuracy and interpretability under identical conditions. Notably, for MSCIAIG-Net with Residual Network 50 Layers as the backbone network verified on FGSC-23, its overall accuracy ( OA ) and average accuracy ( AA ) improves by 6.79 % and 5.54 %, respectively.

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Corona discharge-driven platform for precise manipulation of high-viscosity dielectric droplets on copper wires

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Sensors and Actuators B: Chemical 2025年 442卷

作者： Tang, Qiang Liu, Manfei Tan, Dengqin Wang, Yan Wang, Ziyi Cui, Xiaxia Zhang, Jia-Han Wang, Chengjun Xue, Changguo State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science & Technology Huainan232001 China School of Artificial Intelligence Anhui University of Science and Technology Hefei231131 China Electronic-Photonic Smart Sensing Device R&D Team Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing School of Electronic Information Engineering Inner Mongolia University Hohhot010021 China School of Artificial Intelligence Anhui Polytechnic University Wuhu241000 China School of Materials Science and Engineering Anhui University of Science and Technology Anhui Huainan232000 China

Dielectric fluids, including polymers, oils, and organic solvents, are integral to applications such as microreactors, optical devices, and thermal management systems due to their stability and resistance to evaporation. However, precise manipulation of dielectric droplets, especially those with high viscosity, remains challenging due to low surface tension and small contact angles. This study introduces a novel corona discharge-driven platform designed for the precise formation, transport, and control of dielectric droplets on one-dimensional copper wires. Utilizing dual needle-tip electrodes, the system generates localized electric fields that facilitate non-contact charge deposition, enabling reliable manipulation of droplets with viscosities up to 30,000 cSt. Comprehensive experiments demonstrate synchronized droplet movement with electrode motion, effective handling of complex geometries, and advanced operations such as droplet merging and splitting. Moreover, dielectric droplets are utilized as micro-containers for performing on-droplet chemical and biological assays. The platform also enables efficient aspiration and release of high-viscosity dielectric fluids, surpassing the capabilities of conventional pipette-based methods. These results highlight the platform's robustness, versatility, and strong potential for integration into next-generation microfluidic and lab-on-a-chip systems. © 2025 Elsevier B.V.

关键词： Bioassay

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Let's play music: Audio-driven performance video generation

arXiv

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arXiv 2020年

作者： Zhu, Hao Li, Yi Zhu, Feixia Zheng, Aihua He, Ran Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China CASIA Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multistaged framework to achieve this new task to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap offers more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and inter-frame temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Music

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Interactive prototype learning and self-learning for few-shot medical image segmentation

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Artificial intelligence in Medicine 2025年 167卷

作者： Yuhui Song Chenchu Xu Boyan Wang Xiuquan Du Jie Chen Yanping Zhang Shuo Li School of Computer Science and Technology Anhui University 230601 Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University 230601 Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center 230051 Hefei China Department of Computer Science and Technology Tsinghua University 100084 Beijing China School of Engineering Case Western Reserve University 44106 Cleveland United States

Few-shot learning alleviates the heavy dependence of medical image segmentation on large-scale labeled data, but it shows strong performance gaps when dealing with new tasks compared with traditional deep learning. Existing methods mainly learn the class knowledge of a few known (support) samples and extend it to unknown (query) samples. However, the large distribution differences between the support image and the query image lead to serious deviations in the transfer of class knowledge, which can be specifically summarized as two segmentation challenges: Intra-class inconsistency and Inter-class similarity, blurred and confused boundaries. In this paper, we propose a new interactive prototype learning and self-learning network to solve the above challenges. First, we propose a deep encoding-decoding module to learn the high-level features of the support and query images to build peak prototypes with the greatest semantic information and provide semantic guidance for segmentation. Then, we propose an interactive prototype learning module to improve intra-class feature consistency and reduce inter-class feature similarity by conducting mid-level features-based mean prototype interaction and high-level features-based peak prototype interaction. Last, we propose a query features-guided self-learning module to separate foreground and background at the feature level and combine low-level feature maps to complement boundary information. Our model achieves competitive segmentation performance on benchmark datasets and shows substantial improvement in generalization ability.

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Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation

arXiv

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arXiv 2022年

作者： Zhao, Yue Zhang, Lingming Liu, Yang Meng, Deyu Cui, Zhiming Gao, Chenqiang Gao, Xinbo Lian, Chunfeng Shen, Dinggang Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China The Chongqing Key Laboratory of Signal and Information Processing Chongqing400065 China Department of Orthodontics Stomatological Hospital of Chongqing Medical University Chongqing401147 China Chongqing Key Laboratory for Oral Diseases and Biomedical Sciences Chongqing401147 China Faculty of Information Technology Macau University of Science and Technology China School of Mathematics and Statistics Xi'an Jiaotong University Xian710049 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200030 China The School of Computer Science The University of Hong Kong 999077 Hong Kong School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation. Github: https://***/ZhangLingMing1/TSGCNet. © 2022, CC BY.

关键词： Scanning

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