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arXiv 2025年

作者： Hu, Shiang Gong, Xiao Huang, Xiaolong Ruan, Jie Valdes-Sosa, Pedro Antonio Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University 111 Jiulong Road Hefei230601 China 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 Cuban Center for Neurocience La Habana Cuba

The resting-state brain networks (RSNs) reflects the functional connectivity patterns between brain modules, providing essential foundations for decoding intrinsic neural information within the brain. It serves as one of the primary tools for describing the spatial dynamics of the brain using various neuroimaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). However, the distribution rules or potential modes of functional connectivity weights in the resting state remain unclear. In this context, we first start from simulation, using forward solving model to generate scalp EEG with four channel densities (19, 32, 64, 128). Subsequently, we construct scalp brain networks using five coupling measures, aiming to explore whether different channel density or coupling measures affect the distribution pattern of functional connectivity weights. Next, we quantify the distribution pattern by calculating the skewness, kurtosis, and Shannon entropy of the functional connectivity network weights. Finally, the results of the simulation were validated in a normative database. We observed that: 1) The functional connection weights exhibit a right-skewed distribution, and are not influenced by channel density or coupling measures;2) The functional connection weights exhibit a relatively uniform distribution, with the potential for volume conduction to affect the degree of uniformity in the distribution;3) Networks constructed using coupling measures influenced by volume conduction exhibit significant correlations between the average connection weight and measures of skewness, kurtosis, and Shannon entropy. This study contributes to a deeper understanding of RSNs, providing valuable insights for research in the field of neuroscience, and holds promise for being associated with brain cognition and disease diagnosis. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetoencephalography

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Spiking Point Transformer for Point Cloud Classification

arXiv

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arXiv 2025年

作者： Wu, Peixi Chai, Bosong Li, Hebei Zheng, Menghua Peng, Yansong Wang, Zeyu Nie, Xuan Zhang, Yueyi Sun, Xiaoyan MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China College of Computer Science and Technology Zhejiang University China Tsingmao Intelligence China School of Software Northwestern Polytechnical University China

Spiking Neural Networks (SNNs) offer an attractive and energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their sparse binary activation. When SNN meets Transformer, it shows great potential in 2D image processing. However, their application for 3D point cloud remains underexplored. To this end, we present Spiking Point Transformer (SPT), the first transformer-based SNN framework for point cloud classification. Specifically, we first design Queue-Driven Sampling Direct Encoding for point cloud to reduce computational costs while retaining the most effective support points at each time step. We introduce the Hybrid Dynamics Integrate-and-Fire Neuron (HD-IF), designed to simulate selective neuron activation and reduce over-reliance on specific artificial neurons. SPT attains state-of-the-art results on three benchmark datasets that span both real-world and synthetic datasets in the SNN domain. Meanwhile, the theoretical energy consumption of SPT is at least 6.4× less than its ANN counterpart. Copyright © 2025, The Authors. All rights reserved.

关键词： Energy efficiency

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TMNRED, A Chinese Language EEG Dataset for Fuzzy Semantic Target Identification in Natural Reading Environments

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Scientific Data 2025年第1期12卷 1-14页

作者： Bai, Yanru Tang, Qi Zhao, Ran Liu, Hongxing Zhang, Shuming Guo, Mingkun Guo, Minghan Wang, Junjie Wang, Changjian Xing, Mu Ni, Guangjian Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin 300072 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin 300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300392 China National University of Defense Technology Hunan Changsha 410000 China

Semantic understanding is central to advanced cognitive functions, and the mechanisms by which the brain processes language information are still being explored. Existing EEG datasets often lack natural reading data specific to Chinese, limiting research on Chinese semantic decoding and natural language processing. This study aims to construct a Chinese natural reading EEG dataset, TMNRED, for semantic target identification in natural reading environments. TMNRED was collected from 30 participants reading sentences sourced from public internet resources and media reports. Each participant underwent 400–450 trials in a single day, resulting in a dataset with over 10 hours of continuous EEG data and more than 4000 trials. This dataset provides valuable physiological data for studying Chinese semantics and developing more accurate Chinese natural language processing models. © The Author(s) 2025.

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第02期 349-358页

作者： Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Institute of Natural Sciences Kanazawa University Faculty of Health Data Science and Graduate School of Medicine Juntendo University NVIDIA AI Technology Center NVIDIA Japan

With the rapid development of digital communication and the widespread use of the Internet of Things, multi-view image compression has attracted increasing attention as a fundamental technology for image data communication. Multi-view image compression aims to improve compression efficiency by leveraging correlations between images. However, the requirement of synchronization and inter-image communication at the encoder side poses significant challenges, especially for constrained devices. In this study, we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding. Our model integrates an encoder network, a quantization module, and a decoder network, to ensure both high compression performance and high-quality image reconstruction. The encoder uses a deep Convolutional Neural Network(CNN) to extract high-level features from the input image, which then pass through the quantization module for further compression before undergoing lossless entropy coding. The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side. Specifically, we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps. Second, we employ a semi-coupled convolution module to extract both shared and specific information in images. Finally, a cross-attention module is employed to fuse mutual information extracted from side information. The effectiveness of our model is validated on various datasets, including KITTI Stereo and Cityscapes. The results highlight the superior compression capabilities of our method, surpassing state-of-the-art techniques.

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Prediction and Reference Quality Adaptation for Learned Video Compression

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IEEE Transactions on Image Processing 2025年 34卷 2285-2300页

作者： Sheng, Xihua Li, Li Liu, Dong Li, Houqiang City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China MoE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition Hefei230027 China on Intelligent Technologies for Comprehensive Transportation Institute of Artificial Intelligence Hefei230000 China

Temporal prediction is one of the most important technologies for video compression. Various prediction coding modes are designed in traditional video codecs. Traditional video codecs will adaptively to decide the optimal coding mode according to the prediction quality and reference quality. Recently, learned video codecs have made great progress. However, they did not effectively address the problem of prediction and reference quality adaptation, which limits the effective utilization of temporal prediction and reduction of reconstruction error propagation. Therefore, in this paper, we first propose a confidence-based prediction quality adaptation (PQA) module to provide explicit discrimination for the spatial and channel-wise prediction quality difference. With this module, the prediction with low quality will be suppressed and that with high quality will be enhanced. The codec can adaptively decide which spatial or channel location of predictions to use. Then, we further propose a reference quality adaptation (RQA) module and an associated repeat-long training strategy to provide dynamic spatially variant filters for diverse reference qualities. With these filters, our codec can adapt to different reference qualities, making it easier to achieve the target reconstruction quality and reduce the reconstruction error propagation. Experimental results verify that our proposed modules can effectively help our codec achieve a higher compression performance. © 1992-2012 IEEE.

关键词： Image coding

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Dataset of long-term multi-site LFP activity with spontaneous chronic seizures in temporal lobe epilepsy rats

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Scientific Data 2025年第1期12卷 1-10页

作者： Ni, Haoqi Yang, Yufang Zhang, Fang Sun, Yuting Zheng, Yongte Zhu, Junming Xu, Kedi Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory of Biomedical Engineering of Education Ministry Department of Biomedical Engineering Zhejiang University Hangzhou China Nanhu Brain-computer interface institute Hangzhou 311100 China Department of neurosurgery The second affiliated hospital Zhejiang University school of medicine Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

The characteristics of refractory epilepsy change with disease progression. However, relevant studies are scarce due to the difficulty in obtaining long-term multi-site data from patients with epilepsy. This work aimed to provide a long-term brain electrophysiological dataset of 15 pilocarpine-treated rats with temporal lobe epilepsy (TLE). The dataset was constituted by multi-site local field potential (LFP) signal recorded from 12 sites in the Papez circuit in TLE, including spontaneous seizures and interictal fragments in the chronic period. The LFP data were saved in MATLAB, stored in the Neurodata Without Borders format, and published on the DANDI Archive. We validated the dataset technically through specific signal analysis. In addition, we provided MATLAB codes for basic analyses of this dataset, including power spectral analysis, seizure onset pattern identification, and interictal spike detection. This dataset could reveal how the electrophysiological and epileptic network properties of the brain of rats with chronic TLE changed during epilepsy development, thus help inform the design of adaptive neuromodulation for epilepsy. © The Author(s) 2025.

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Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model

arXiv

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arXiv 2025年

作者： Yang, Ziyuan Chen, Yingyu Wang, Zhiwen Shan, Hongming Chen, Yang Zhang, Yi College of Computer Science Sichuan University China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University China School of Cyber Science and Engineering Sichuan University China School of Computer Science and Software Engineering Southwest Petroleum University China Institute of Science and Technology for Brain-inspired Intelligence Fudan University China School of Computer Science and Engineering Southeast University China

Reducing radiation doses benefits patients, however, the resultant low-dose computed tomography (LDCT) images often suffer from clinically unacceptable noise and artifacts. While deep learning (DL) shows promise in LDCT reconstruction, it requires large-scale data collection from multiple clients, raising privacy concerns. Federated learning (FL) has been introduced to address these privacy concerns;however, current methods are typically tailored to specific scanning protocols, which limits their generalizability and makes them less effective for unseen protocols. To address these issues, we propose SCAN-PhysFed, a novel SCanning- and ANatomy-level personalized Physics-Driven Federated learning paradigm for LDCT reconstruction. Since the noise distribution in LDCT data is closely tied to scanning protocols and anatomical structures being scanned, we design a dual-level physics-informed way to address these challenges. Specifically, we incorporate physical and anatomical prompts into our physics-informed hypernetworks to capture scanning- and anatomy-specific information, enabling dual-level physics-driven personalization of imaging features. These prompts are derived from the scanning protocol and the radiology report generated by a medical large language model (MLLM), respectively. Subsequently, client-specific decoders project these dual-level personalized imaging features back into the image domain. Besides, to tackle the challenge of unseen data, we introduce a novel protocol vector-quantization strategy Copyright © 2025, The Authors. All rights reserved.

关键词： computerized tomography

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi'an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. The code and results of our method are available at https://***/rmcong/TRNetTCSVT2025. © 1991-2012 IEEE.

关键词： HTTP

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Multi-View Self-Supervised Learning Enhances Automatic Sleep Staging from EEG Signals

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Yu, Tianyou Hu, Xinxin He, Yanbin Wu, Wei Gu, Zhenghui Yu, Zhuliang Li, Yuanqing Wang, Fei Xiao, Jun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Pazhou Laboratory Research Center for Brain-Computer Interface Guangzhou510330 China Guangdong Work Injury Rehabilitation Hospital Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangzhou510440 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China South China Normal University School of Software Guangzhou510631 China South China University of Technology School of Electric Power Engineering Guangzhou510640 China

Deep learning-based methods for automatic sleep staging offer an efficient and objective alternative to costly manual scoring. However, their reliance on extensive labeled datasets and the challenge of generalization to new subjects and datasets limit their widespread adoption. Self-supervised learning (SSL) has emerged as a promising solution to address these issues by learning transferable representations from unlabeled data. This study highlights the effectiveness of SSL in automated sleep staging, utilizing a customized SSL approach to train a multi-view sleep staging model. This model includes a temporal view feature encoder for raw EEG signals and a spectral view feature encoder for time-frequency features. During pretraining, we incorporate a cross-view contrastive loss in addition to a contrastive loss for each view to learn complementary features and ensure consistency between views, enhancing the transferability and robustness of learned features. A dynamic weighting algorithm balances the learning speed of different loss components. Subsequently, these feature encoders, combined with a sequence encoder and a linear classifier, enable sleep staging after finetuning with labeled data. Evaluation on three publicly available datasets demonstrates that finetuning the entire SSL-pretrained model achieves competitive accuracy with state-of-the-art methods—86.4%, 83.8%, and 85.5% on SleepEDF-20, SleepEDF-78, and MASS datasets, respectively. Notably, our framework achieves near-equivalent performance with only 5% of the labeled data compared to full-label supervised training, showcasing SSL's potential to enhance automated sleep staging efficiency. © 1964-2012 IEEE.

关键词： Self-supervised learning

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Visual cognition of birds and its underlying neural mechanism:A review

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Avian Research 2022年第2期13卷 216-225页

作者： Xiaoke Niu Zhenyang Jiang Yanyan Peng Shuman Huang Zhizhong Wang Li Shi Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical EngineeringZhengzhou UniversityZhengzhou450001China Department of Automation Tsinghua UniversityBeijing100084China

Birds have acute vision and many remarkable visual cognition abilities,due to their unique living *** underlying neural mechanisms have also attracted interests of researchers in ***,we firstly summarize the visual cognition abilities of birds,and make a comparison with ***,the underlying neural mechanisms are presented,including histological structure of avian brain and visual pathways,typical experimental results and conclusions in electrochemistry and *** latter mainly focuses on several higher brain areas related to visual cognition,including mesopallium ventrolaterale,entopallium,visual Wulst,and nidopallium ***,we make a conclusion and provide a suggestion about future studies on revealing the neural mechanisms of avian visual *** review presents a detailed understanding of avian visual cognition and would be helpful in ornithology studies in the field of cognitive neuroscience.

关键词： Avian Electrophysiology Neural mechanism Visual cognition

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