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Learned distributed image compression with decoder side information

Digital Communications and Networks 2025年第2期11卷 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.

关键词： Digital communication Image compression Side information Channel-spatial attention module Cross-attention module

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Attention Detection Using EEG Signals and Machine Learning: A Review

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Machine Intelligence Research 2025年第2期22卷 219-238页

作者： Qianru Sun Yueying Zhou Peiliang Gong Daoqiang Zhang Key Laboratory of Brain-Machine Intelligence Technology Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Attention detection using electroencephalogram(EEG)signals has become a popular ***,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG ***,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level ***,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these ***,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning *** also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and ***,we discuss the existing challenges and future trends in this field.

关键词： Attention detection electroencephalogram(EEG) machine learning deep learning brain-computer interface.

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A Wireless Fully Implantable DBS Device for Closed-Loop Neuromodulation Applications

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IEEE Sensors Journal 2025年第9期25卷 15331-15344页

作者： Su, Kangyu Qiu, Zhang Xu, Kedi Xu, Jian Wang, Yueming Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University MOE Frontier Science Center for Brain Science and Brain-Machine Integration Hangzhou310027 China Nanhu Brain-Computer Interface Institute Hangzhou311100 China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou310027 China Zhejiang University School of Medicine Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Hangzhou310013 China Zhejiang University State Key Laboratory of Brain-Machine Intelligence Hangzhou310027 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Closed-loop deep brain stimulation (DBS) is an emerging technology in the field of neuromodulation, which can provide effective treatment methods for intractable neurological disorders. To address the faced challenges, this article presents a high-precision closed-loop DBS device based on a custom-designed neuromodulation integrated circuit (CNIC) fabricated in a 180-nm bipolar-CMOS-DMOS (BCD) process. Also, three new techniques have been developed to optimize system performance. First, to improve system stability, a frequency shaping & quick blanking amplifier (FSQBA)-based analog front-end is proposed to extend system dynamic range (DR). Second, both stimulation artifacts (SAs) suppression scheme and closed-loop control algorithm are developed to achieve real simultaneous recording and stimulation. Third, a redundant-crossfire (RXF) technique is designed to enhance current precision without consuming much extra area. Bench-top measurements show that the designed system features a wide DR of 112 dB, a short saturation recovery time of 100 μs, and a high stimulation current resolution of 13.1-bit. Besides, animal experiments demonstrate that the designed system is highly suitable for long-term implantation applications, thus providing accurate and reliable closed-loop control for neuromodulation therapies. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Visual numerical cognition in pigeons: conformity to the Weber–Fechner law

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Animal Cognition 2025年第1期28.0卷 1-13页

作者： Wu, Peng Zhu, Juncai He, Qingzhi Wang, Zhizhong Shi, Li School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China Department of Automation Tsinghua University Beijing 100084 China

As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber–Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber–Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution. © The Author(s) 2025.

关键词： Birds Logarithm Mental number line Numerosity Pigeons

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Multimodal brain-computer interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： brain computer interface

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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

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

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, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Machine learning assisted screening of binary alloys for metal-based anode materials

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Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310058ZhejiangChina Nanhu Brain-Computer Interface Institute Hangzhou 311100ZhejiangChina Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-TaizhouTaizhou 318000ZhejiangChina Inner Mongolia Electric Power Economic and Technological Research Institute Hohhot 010010Inner MongoliaChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014ZhejiangChina School of Engineering Hangzhou City UniversityHangzhou 310015ZhejiangChina Institute of Fundamental and Transdisciplinary Research Zhejiang UniversityHangzhou 311121China

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***,experimental investigations of metal alloys can be time-consuming and expensive,often requiring extensive experimental design and *** this study,we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN)to screen alloy anode materials for seven battery systems,including lithium(Li),sodium(Na),potassium(K),zinc(Zn),magnesium(Mg),calcium(Ca),and aluminum(Al).We utilized data with tens of thousands of alloy materials from the Materials Project(MP)and Automatic FLOW for Materials Discovery(AFLOW)*** any experimental voltage input,we identified over 30 alloy systems that have been experimentally validated with good ***,we predicted over 100 alloy anodes with low potential and high specific *** hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： CGCNN Alloy anodes Machine learning Metal-based batteries

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A survey of motor rehabilitation for hemiplegic upper limbs based on the brain–apparatus interaction

Intelligent Sports and Health

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Intelligent Sports and Health 2025年第1期1卷 4-10页

作者： Yangsong Zhang Xinrui Bian Fali Li Yufeng Tang Ting Zeng Jing Zhao Dezhong Yao Peng Xu School of Computer Science and Technology Laboratory for Brain Science and Artificial Intelligence Southwest University of Science and Technology Mianyang 621010 China Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Scienceand Technology of China Chengdu 611731 China Mianyang Central Hospital Mianyang 621000 China Radiation Oncology Key Laboratory of Sichuan Province Chengdu 610041 China

Stroke remains one of the leading causes of disability among adults, and the demand for stroke rehabilitation services is increasing. Many studies in the literature have provided various rehabilitation training strategies for stroke patients. In this work, we focus on motor rehabilitation for hemiplegic upper limbs with the assistance of a brain–computer interface and an exoskeleton. We reviewed the progress from multiple perspectives, i.e., the rehabilitation training mode, the rehabilitation movement paradigm, decoding algorithms for physiological signals, motion-assisted rehabilitation robots, evaluation strategies and brain mechanisms. Furthermore, we briefly discuss some challenges and list several future research directions. We hope that this paper will provide insights into future studies on upper limb rehabilitation for hemiplegic patients.

关键词： Hemiplegic upper limbs Smart rehabilitation EMG EEG brain–apparatus interaction

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Dynamic and low-dimensional modeling of brain functional connectivity on Riemannian manifolds

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NeuroImage 2025年 314卷 121243-121243页

作者： Wang, Mingyu Wang, Yueming Yang, Yuxiao MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China School of Computer Science and Technology Zhejiang University Hangzhou China State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China

Modeling brain functional connectivity (FC) is key in investigating brain functions and dysfunctions. FC is typically quantified by symmetric positive definite (SPD) matrices that are located on a Riemannian manifold rather than the regular Euclidean space, whose modeling faces three challenges. First, FC can be time-varying and the temporal dynamics of FC matrix time-series need to be modeled within the constraint of the SPD Riemannian manifold geometry, which remains elusive. Second, the FC matrix time-series exhibits considerable stochasticity, whose probability distribution is difficult to model on the Riemannian manifold. Third, FC matrices are high-dimensional and dimensionality reduction methods for SPD matrix time-series are still lacking. Here, we develop a Riemannian state-space modeling framework to simultaneously address the challenges. First, we construct a new Riemannian state-space model (RSSM) to define a hidden SPD matrix state to achieve dynamic, stochastic, and low-dimensional modeling of FC matrix time-series on the SPD Riemannian manifold. Second, we develop a new Riemannian Particle Filter (RPF) algorithm to estimate the hidden low-dimensional SPD matrix state and predict the FC matrix time-series. Third, we develop a new Riemannian Expectation Maximization (REM) algorithm to fit the RSSM parameters. We evaluate the proposed RSSM, RPF, and REM using simulation and real-world EEG datasets, demonstrating that the RSSM enables accurate prediction of the EEG FC time-series and classification of emotional states, outperforming traditional Euclidean methods. Our results have implications for modeling brain FC on the SPD Riemannian manifold to study various brain functions and dysfunctions. © 2025 The Authors

关键词： brain functional connectivity Dimensionality reduction EEG Riemannian manifold Stochastic matrix dynamics

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Development of yellow-light TiO2 integrated photonics

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Optics Letters 2025年第11期50卷 3489-3492页

作者： Zequn Chen Yiheng Tang Maoliang Wei Xiaojing Li Boshu Sun Yingchun Wu Sishuo Zou Ji Huang Ke Si Wei Gong Hongtao Lin Lan Li Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou 310024 China Zhejiang Key Laboratory of 3D Micro/Nano Fabrication and Characterization School of Engineering Westlake University Hangzhou 310030 China The State Key Lab of Brain-Machine Intelligence College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China MOE Frontier Science Center for Brain Science & Brain-Machine Integration Zhejiang University Hangzhou 310027 China Nanhu Brain-Computer Interface Institute Hangzhou 311100 China College of Optical Science and Engineering Zhejiang University Hangzhou 310027 China Westlake Institute for Optoelectronics Fuyang Hangzhou Zhejiang 311421 China

Visible-light photonic integrated circuits (PICs) are rapidly emerging as a crucial technology to overcome the scaling challenges in quantum information processing and biosensing. Titanium dioxide (TiO 2 ), with its high transparency in the visible range, is regarded as a promising material for facilitating the development of high-performance visible-light integrated systems. In this paper, we introduce a series of passive TiO 2 integrated devices, which include waveguides, multimode interferometers (MMIs), and thermo-optic switches (TOSs), monolithically integrated for the first time to our knowledge at a working wavelength of 589 nm. A propagation loss of 5.46 ± 0.17 dB/cm demonstrates a significant improvement over previous reports on yellow-light TiO 2 waveguides. This study marks the first fabrication of a TOS operating at 589 nm, featuring a TiO 2 core and polymer cladding. Notably, it achieves a power consumption (P π ) of 8.24 mW, which represents a threefold reduction compared to silicon nitride-based TOSs operating in the visible spectrum. These results offer new insight into the design and fabrication of visible waveguide-integrated devices, paving the way for future emerging applications, such as quantum information, sensors, and biomedical science.

关键词： Biosensors Effective refractive index Information processing Photonics Quantum information Titanium dioxide

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