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arXiv 2022年

作者： Chen, Jianbo Zhang, Yangsong Pan, Yudong Xu, Peng Guan, Cuntai Laboratory for Brain Science and Medical Artificial Intelligence School of Computer Science and Technology Southwest University of Science and Technology Mianyang China MOE Key Laboratory for NeuroInformation Clinical Hospital of Chengdu Brain Science Institute Center for Information in BioMedicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Nanyang Technological University Singapore

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data become urgent. In recent years, developing the methods that can work in inter-subject classification scenario has become a promising new direction. As the popular deep learning model nowadays, Transformer has excellent performance and has been used in EEG signal classification tasks. Therefore, in this study, we propose a deep learning model for SSVEP classification based on Transformer structure in inter-subject classification scenario, termed as SSVEPformer, which is the first application of the transformer to the classification of SSVEP. Inspired by previous studies, the model adopts the frequency spectrum of SSVEP data as input, and explores the spectral and spatial domain information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) is proposed to further improve the classification performance. Experiments were conducted using two open datasets © 2022, CC BY.

关键词： Classification (of information)

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Implantable brain-computer interface Based On Printing technology

Implantable Brain-Computer Interface Based On Printing Techn...

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International Winter Workshop on brain-computer interface (BCI)

作者： Lusheng Liu Bo Wen Minjie Wang Aiping Wang Jing Zhang Yuan Zhang Song Le Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of Meta-Medical Academy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Implantable brain-computer interface is a braincomputer interaction device by implants optrodes/electrodes on the surface or deep of the brain. By stimulating and recording neural signals, we can explore the function of neural tissue and further guide disease treatment or brain signal decoding. This article reviews the development of printed electronics in implantable brain-computer interfaces, mainly discussing the advantages and disadvantages of printed electrodes in braincomputer interfaces and the possibility of printing technology used in optogenetic brain-computer interfaces, to illuminate a great prospect of printing technology in implantable braincomputer interfaces.

关键词： Printing Electrodes Implants Organic light emitting diodes brain-computer interfaces Optogenetics Recording

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Cross View Capture for Distributed Image Compression with Decoder Side Information

Cross View Capture for Distributed Image Compression with De...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yankai Yin Zhe Sun Peiying Ruan Feng Duan Ruidong Li Chi Zhu Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Institute of Natural Sciences Kanazawa University Ishikawa Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo University Chiba Japan NVIDIA AI Technology Center NVIDIA Japan Tokyo Japan Department of Systems Life Engineering Maebashi Institute of Technology Maebashi Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Image compression is increasingly important in applications like intelligent driving and smart surveillance systems. This study presents a novel cross view capture distributed image compression network (CVCDIC) to improve the compression quality by using decoder side information. The CVCDIC’s decoder utilizes feature extraction networks to extract features from both the primary image and the side information. Furthermore, a multi-level cross view attention module is designed to capture interrelated details between images at multiple hierarchical levels. Finally, a spatial refinement module, constructed on the foundation of information distillation networks, is designed to further refine the quality of reconstructed images. The results show that CVCDIC can achieve an MS-SSIM of 0.978 at 0.15 bpp, surpassing DSIN (0.925), NDIC (0.956), and ATN (0.955) on the KITTI Stereo dataset.

关键词： Measurement Image coding Fuses Surveillance Rate-distortion Feature extraction Decoding

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Current understanding of metal ions in the pathogenesis of Alzheimer’s disease

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Translational Neurodegeneration 2020年第2期9卷 105-117页

作者： Lu Wang Ya-Ling Yin Xin-Zi Liu Peng Shen Yan-Ge Zheng Xin-Rui Lan Cheng-Biao Lu Jian-Zhi Wang Key Laboratory of Brain Research of Henan Province Sino-UK Joint Laboratory of Brain Function and Injury of Henan ProvinceDepartment of Physiology and NeurobiologyXinxiang Medical UniversityXinxiang453003China Department of Pathophysiology School of Basic MedicineKey Laboratory of Ministry of Education of China for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430030China

Background The homeostasis of metal ions,such as iron,copper,zinc and calcium,in the brain is crucial for maintaining normal physiological *** have shown that imbalance of these metal ions in the brain is closely related to the onset and progression of Alzheimer’s disease(AD),the most common neurodegenerative disorder in the *** body Erroneous deposition/distribution of the metal ions in different brain regions induces oxidative *** metal ions imbalance and oxidative stress together or independently promote amyloid-β(Aβ)overproduction by activatingβ-orγ-secretases and inhibitingα-secretase,it also causes tau hyperphosphorylation by activating protein kinases,such as glycogen synthase kinase-3β(GSK-3β),cyclin-dependent protein kinase-5(CDK5),mitogen-activated protein kinases(MAPKs),etc.,and inhibiting protein phosphatase 2A(PP2A).The metal ions imbalances can also directly or indirectly disrupt organelles,causing endoplasmic reticulum(ER)stress;mitochondrial and autophagic dysfunctions,which can cause or aggravate Aβand tau aggregation/accumulation,and impair synaptic *** worse,the metal ions imbalance-induced alterations can reversely exacerbate metal ions misdistribution and *** vicious cycles between metal ions imbalances and Aβ/tau abnormalities will eventually lead to a chronic neurodegeneration and cognitive deficits,such as seen in AD *** The metal ions imbalance induces Aβand tau pathologies by directly or indirectly affecting multiple cellular/subcellular pathways,and the disrupted homeostasis can reversely aggravate the abnormalities of metal ions transportation/***,adjusting metal balance by supplementing or chelating the metal ions may be potential in ameliorating AD pathologies,which provides new research directions for AD treatment.

关键词： Metal ions Alzheimer's disease Amyloid-β Tau Oxidative stress Autophagy Synapses

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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

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IEEE Transactions on Network science and Engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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Removing EOG Artifacts from the Resting State EEG Signal of Methamphetamine Addicts by ICA Algorithms

Removing EOG Artifacts from the Resting State EEG Signal of ...

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International Winter Workshop on brain-computer interface (BCI)

作者： Gege Zhan Haolong Su Pengchao Wang Lan Niu Jianxiong Bin Wei Mu Xueze Zhang Haifeng Jiang Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Zhejiang Lab Research Center for Intelligent Sensing Hangzhou China

EEG signal contains a wealth of information about brain activity, but the recording process is inevitably contaminated by EOG artifacts. An effective method to remove EOG artifacts can provide a guarantee for subsequent EEG analysis. In this paper, we compare the performance of four ICA algorithms in removing EOG artifacts from EEG signals of methamphetamine addicts. From the perspective of time domain and power spectral density, all the four algorithms can effectively remove the EOG artifacts without obvious difference. In terms of PSNR, MI and processing speed, FastICA algorithm can achieve higher processing speed and reconstruct signals better than the other three algorithms.

关键词： Electrooculography brain Electroencephalography brain-computer interfaces Recording Time-domain analysis

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Effects of Input Neuron Mapping Coordinates in Spiking Neural Network on the Motor Imagery EEG Signals Classification

Effects of Input Neuron Mapping Coordinates in Spiking Neura...

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International Winter Workshop on brain-computer interface (BCI)

作者： Gege Zhan Haolong Su Pengchao Wang Lan Niu Jianxiong Bin Wei Mu Xueze Zhang Haifeng Jiang Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Spiking neural network (SNN), the third generation of the artificial neural network, uses the same computational principles as Spatio-temporal brain data (STBD) generation, so it can be used as an effective tool to learn and understand STBD. When using SNN to process STBD, mapping of input neurons is a problem to be considered. This paper investigates the influence of changing the mapping coordinates of input neurons on motor imagery EEG signal classification. The results show that the coordinate change affects the interaction between the input neuron and its neighboring neurons, and causes the classification accuracy to change, but the impact mechanism is unknown.

关键词： Neurons Pattern classification Artificial neural networks Electroencephalography brain-computer interfaces Biological neural networks

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Face preference detection task based on EEG energy analysis in the source domain

Face preference detection task based on EEG energy analysis ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Pengchao Wang Junkongshuai Wang Lu Wang Wei Mu Gege Zhan Xueze Zhang Lan Niu Jianxiong Bin Zhiyan Dong Lihua Zhang Jie Jia Zhongxue Gan Xiaoyang Kang MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics Institute of AI and Robotics Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Yiwu Research Institute of Fudan University Yiwu City China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

Facial stimulation can produce specific event-related potential (ERP) component N170 in the fusiform gyrus region. However, the role of the fusiform gyrus region in facial preference tasks is not clear at present, and the current research of facial preference analysis based on EEG signals is mostly carried out in the scalp domain. This paper explores whether the region of the fusiform gyrus is involved in processing face preference emotions in terms of the distribution of energy over the source domain, and finds that the pars orbitalis cortex is most energetically active in the face preference task and that there are significant differences between the left and right *** Relevance— The role of pars orbitalis in facial preference may help doctors determine whether the pars orbitalis cortex is lost in clinical practice.

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Distribution of Electrical Stimulation Current of a Minimally Invasive Endovascular Stent-Electrode

Distribution of Electrical Stimulation Current of a Minimall...

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International Winter Workshop on brain-computer interface (BCI)

作者： Bo Wen Jing Zhang Minjie Wang Lusheng Liu Yuan Zhang Aiping Wang Lu Wang Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering &Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The implantable brain-computer interface is a kind of brain-computer interaction device. The electrode array implanted in the deep surface of the brain is used to study neural function and medical rehabilitation. Nowadays, the implanted type usually has different degrees of damage to the brain, and the intravascular electrode, as the best technology, has the advantage of no need to drill holes in the brain, and the EEG signal collected is also strong, so it has become a very promising direction. This paper mainly discusses the intravascular use of vascular stent as a carrier, the attachment of the electrode array to the vascular stent, and the multi-physical field simulation of the array in the superior sagittal sinus. By discussing the layout of different electrode arrays and the discussion of grounding electrodes, this paper summarizes a relatively reasonable electrode array layout and a relatively reasonable selection of grounding electrodes.

关键词： Electrodes Electric potential Minimally invasive surgery Grounding Layout Interference Electrical stimulation

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A Channel Selection Method for Motor Imagery EEG Based on Fisher Score of OVR-CSP

A Channel Selection Method for Motor Imagery EEG Based on Fi...

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International Winter Workshop on brain-computer interface (BCI)

作者： Wei Mu Junkongshuai Wang Lu Wang Pengchao Wang Jiaguan Han Lan Niu Jianxiong Bin Lusheng Liu Jing Zhang Jie Jia Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

EEG is widely used in the field of brain computer interfaces. It has the advantages of being non-invasive, easy acquisition, and so on. However, multi-channel EEG signals will bring noise interference and redundant information while improving the resolution, and channel selection can reduce noise signals to obtain more real signals. Therefore, appropriate channel selection is very necessary for BCI system application. This paper proposes a Fisher score calculation method based on OVR-CSP features for channel selection and uses a transformer to classify. The experimental results show that the proposed method achieves 85.54% accuracy in selecting 16.55 channels on the public dataset BCI IV IIa.

关键词： Electrodes Interference Transformers Feature extraction Electroencephalography brain-computer interfaces Physiology

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