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International Winter Workshop on brain-computer interface (BCI)

作者： Liang Shen Bo Wen Zihua Chen Minjie Wang Linlin Han Bo Shen Kai Cao Lihua Zhang Jianjun Wu 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 Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders State Key Laboratory of Medical Neurobiology Huashan Hospital Fudan University Shanghai China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China

This review delves into the emerging field of endovascular stent electrodes in the realm of brain-computer interfaces, highlighting advancements in their fabrication methods, signal acquisition performance, electrical stimulation effects, and research progress in electrochemical characteristics, mechanical stability, and biocompatibility. In comparison to traditional invasive procedures, endovascular stent electrodes, implanted through minimally invasive means, mitigate the risks associated with extensive trauma and tissue inflammation, demonstrating commendable signal acquisition capabilities. While exhibiting a slight deficiency in visual evoked potentials, they showcase accuracy in the acquisition and decoding of motor perception signals, rivaling or surpassing traditional surgical methods. Additionally, the technology shows potential therapeutic efficacy in electrical stimulation applications. Nevertheless, challenges such as electrical stimulation thresholds and visibility issues warrant further research and refinement. In summary, endovascular stent electrodes, as an emerging avenue in brain-computer interfaces, hold promise for significant breakthroughs in future research, opening up new possibilities in the field of neuroscience.

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Alleviating cognitive impairments in bipolar disorder with a novel DTI-guided multimodal neurostimulation protocol: a double-blind randomized controlled trial

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BMC Medicine 2025年第1期23卷 1-14页

作者： Wang, Minmin Zhou, Hetong Zhang, Xiaomei Chen, Qianfeng Tong, Qiqi Han, Qiai Zhao, Xudong Wang, Dandan Lai, Jianbo He, Hongjian Zhang, Shaomin Hu, Shaohua Department of Psychiatry The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Westlake Institute for Optoelectronics Westlake University Hangzhou China Nanhu Brain-Computer Interface Institute Hangzhou China Zhejiang Key Laboratory of Precision Psychiatry Hangzhou China Department of Psychiatry Huzhou Third Municipal Hospital Huzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China School of Physics Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China Brain Research Institute of Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou China

Background: Traditional neuromodulation strategies show promise in enhancing cognitive abilities in bipolar disorder (BD) but remain suboptimal. This study introduces a novel multimodal neurostimulation (MNS) protocol to improve therapeutic outcomes. Methods: The novel MNS protocol used individualized diffusion tensor imaging (DTI) data to identify fiber tracts between the dorsolateral prefrontal cortex and dorsal anterior cingulate cortex. The highest structural connectivity point is selected as the individualized stimulation site, which is then targeted using a combination of optimized transcranial alternating current stimulation (tACS) and robot-assisted navigated repetitive transcranial magnetic stimulation (rTMS). A double-blind randomized controlled trial was conducted to investigate the clinical efficacy of this innovative neuromodulation approach on cognitive abilities in stable-phase BD patients. One hundred BD patients were randomly assigned to four groups: group A (active tACS-active rTMS (MNS protocol)), group B (sham tACS-active rTMS), group C (active tACS-sham rTMS), and group D (sham tACS-sham rTMS). Participants underwent 15 sessions over 3 weeks. Cognitive assessments (THINC integrated tool) were conducted at baseline (week 0) and post-treatment (week 3). Results: Sixty-six participants completed all 15 sessions. Group A (MNS protocol) showed superior improvements in Spotter CRT, TMT, and DSST scores compared to other groups at week 3. Only group A exhibited significant activation in the left frontal region post-MNS intervention. The novel MNS protocol was well tolerated, with no significant side effects observed. Conclusions: The study indicates that DTI-guided multimodal neurostimulation mode significantly improves cognitive impairments and is safe for stable-phase BD patients. Trial registration: *** identifier: NCT05964777. © The Author(s) 2025.

关键词： Diffusion tensor imaging Electric field Individualization Repetitive transcranial magnetic stimulation Transcranial alternating current stimulation

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Differences of LFP Rhythms in the SGP brain Region When Approaching Pigeons at Different Distances

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Science Technology and Engineering 2024年第25期24卷 10708-10714页

作者： Shang, Zhi-Gang Du, Xu-Xu Yang, Li-Fang Li, Meng-Meng Electrical and Information Engineering College Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-computer Interface Technology Zhengzhou450001 China

The study of fearful emotions has important implications for animal emotional control and development. Current effects of fearful emotions on the behavior and brain activity of animals (especially birds) are mostly biased towards behavioral and physiological phenomena, and there is a lack of studies involving dynamic analysis of the patterns of neural electrical activity within specific relevant nuclei. In order to understand the rule of change of neural signals in pigeons under the fear emotion, taking pigeons as a model animal and its stratum griseum periventriculare (SGP) nuclei as the target brain area, adopting the experimental method of gradual approach of danger to study the pattern of change of neural activities under different fear proximity, the rhythmic differences of frequency domain, time-frequency energy, and functional connectivity based on the signal of Local field potential (LFP) were analyzed in-depth. The LFP signals of 2 pigeons in the resting state and the fear looming experiment were firstly collected and the four characteristic rhythmic components, Delta, Theta, Beta and Gamma, were extracted. The autoregressive modeling method was used to calculate the difference in the percentage of power spectral density between the quiet and fearful states at each rhythm. Next, the idiosyncratic change in time-frequency energy in the fear state with change in looming distance was analyzed. A coherence-based functional brain network was also constructed, and the local network connectivity characteristics of pigeons in both quiet and fearful states were statistically analyzed using a two-factor analysis of variance (ANOVA) method. Finally, a linear fitting analysis was performed to analyze the relationship between the network topological characteristics and the looming distance. The results show that the power spectral occupancy of the Theta band and the strength of brain functional network connections are significantly increased in the fear looming state of

关键词： Analysis of variance (ANOVA)

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Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis:Convergence and Challenges

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Precision Chemistry 2024年第10期2卷 518-538页

作者： Yuxin Yang Yueqi Li Longhua Tang Jinghong Li State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027China Nanhu Brain-Computer Interface Institute HangzhouZhejiang 311100China Center for BioAnalytical Chemistry Hefei National Laboratory of Physical Science at MicroscaleUniversity of Science and Technology of ChinaHefei 230026China Department of Chemistry Center for BioAnalytical ChemistryKey Laboratory of Bioorganic Phosphorus Chemistry&Chemical BiologyTsinghua UniversityBeijing 100084China Beijing Institute of Life Science and Technology Beijing 102206ChinaNew Cornerstone Science InstituteBeijing 102206China

Single-molecule bioelectronic sensing,a groundbreaking domain in biological research,has revolutionized our understanding of molecules by revealing deep insights into fundamental biological *** advent of emergent technologies,such as nanogapped electrodes and nanopores,has greatly enhanced this field,providing exceptional sensitivity,resolution,and integration ***,challenges persist,such as complex data sets with high noise levels and stochastic molecular *** intelligence(AI)has stepped in to address these issues with its powerful data processing *** algorithms effectively extract meaningful features,detect subtle changes,improve signal-to-noise ratios,and uncover hidden patterns in massive *** review explores the synergy between AI and single-molecule bioelectronic sensing,focusing on how AI enhances signal processing and data analysis to boost accuracy and *** also discuss current limitations and future directions for integrating AI,highlighting its potential to advance biological research and technological innovation.

关键词： single-molecule bioelectronic sensing artificial intelligence machine learning signal processing biosensors

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A Compact Dead-Zone-Free Multi-Channel Stimulation and Recording System for Precision Peripheral Nerve Modulation 46

A Compact Dead-Zone-Free Multi-Channel Stimulation and Recor...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Xing, Yanjie Chen, Weihuang Yu, Danlei Gao, Xiang Bao, Jieting Xu, Kedi Zhejiang China 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 Institute of Advanced Digital Technology and Instrument Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Zhejiang University Hangzhou310027 China Binjiang Institude of Zhejiang University Hangzhou310027 China Nanhu Brain-computer interface institute Hangzhou311100 China Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9798350371499

Peripheral nerve modulation has a certain potential in motor recovery, but the majority of systems suffer from the drawback of dead time caused by switching channels to achieve multi-channel stimulation. As a response to this challenge, we have developed a peripheral nerve modulation device with 64-channel recording and 16-channel stimulation without dead time. Through modulation of the rat sciatic nerve, we identified the crucial role of initial phase and amplitude in the outcomes of dual-channel combined stimulation. This design not only enhanced neural activation capabilities with constant power supply but also achieves more precise neural activation and reduces unintentional activation. © 2024 IEEE.

关键词： Recording instruments

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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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Research on EEG Signal Classification Method Based on brain-computer interface Control of Rehabilitation Robot

Research on EEG Signal Classification Method Based on Brain-...

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Rehabilitation Engineering and Assistive Technology (i-CREATe), International Convention on

作者： Yiwen Zhang Wenzhi Wang Gan Liu Yi Qiao Ruochuan Shi Yahan Duan Ying Du Sining Li Fengrui Guo Thomas John McHugh Feng Duan Tianjin key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin P. R. China Laboratory for Circuit and Behavioral Physiology RIKEN Center for Brain Science Wakoshi Saitama Japan Department of Life Sciences Graduate School of Arts and Sciences The University of Tokyo Tokyo Japan

ISBN: (数字)9798350355154

ISBN: (纸本)9798350355161

brain-computer interface (BCI) control of multiple rehabilitation robots provides a novel type of human-robot interaction and an important research direction in the field of intelligent rehabilitation. However, most current EEG signal classification algorithms for controlling rehabilitation robots exhibit several drawbacks, including a lack of novelty, insufficient classification accuracy and poor adaptability to different experimental paradigms. Therefore, we designed a new deep-learning method to classify EEG signals to enhance the effectiveness of BCI control of rehabilitation robots. Here we apply this method to classify EEG signals and control the rehabilitation robot to accomplish a target task. In addition, we compare our algorithm with several different EEG signal classification algorithms in two experimental paradigms: motor imagery and motor execution. Our deep learning method has the highest offline accuracy of 72.7% and an average accuracy of 72.4% under the motor imagery experimental paradigm, and the highest offline accuracy of 97.5% and an average accuracy of 90.4% under the motor execution experimental paradigm. These results show that our EEG signal classification algorithms is more effective compared to other EEG signal classification algorithms and can effectively be used to control a rehabilitation robot across a wider range of applications.

关键词： Deep learning Accuracy Robot control Pattern classification Human-robot interaction Assistive robots Motors Electroencephalography brain-computer interfaces Classification algorithms

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A review on the applications of graph neural networks in materials science at the atomic scale

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Materials Genome Engineering Advances 2024年第2期2卷 1-19页

作者： Xingyue Shi Linming Zhou Yuhui Huang Yongjun Wu Zijian Hong School of Materials Science and Engineering Zhejiang UniversityHangzhouZhejiangChina Nanhu Brain-Computer Interface Institute HangzhouZhejiangChina Research Institute of Zhejiang University-Taizhou TaizhouZhejiangChina State Key Laboratory of Silicon and Advanced Semiconductor Materials Zhejiang UniversityHangzhouZhejiangChina

In recent years,interdisciplinary research has become increasingly popular within the scientific *** fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer *** neural networks(GNNs)are new machine learning models with powerful feature extraction,relationship inference,and compositional generalization *** advantages drive researchers to design computational models to accelerate material property prediction and new materials design,dramatically reducing the cost of traditional experimental *** review focuses on the principles and applications of the *** basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural ***,the principles and highlights of seven classic GNN models,namely crystal graph convolutional neural networks,iCGCNN,Orbital Graph Convolutional neural Network,MatErials Graph Network,Global Attention mechanism with Graph neural Network,Atomistic Line Graph neural Network,and BonDNet are *** connections and differences are also ***,insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.

关键词： CGCNN graph neural networks MACHINE learning materials design property prediction

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Identifying extracerebellar characteristics in a large cohort of 319 Chinese patients with spinocerebellar ataxia type 3

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Chinese Medical Journal 2024年第17期137卷 2131-2133页

作者： Donghui Li Jinyang Yu Chenhao Zhu Yichu Du Yin Ma Yi Dong Zhiying Wu Department of Medical Genetics and Center for Rare Diseases and Department of Neurology in Second Affiliated Hospitaland Zhejiang Key Laboratory of Rare Diseases for Precision Medicine and Clinical TranslationZhejiang University School of MedicineHangzhouZhejiang 310009China Nanhu Brain-Computer Interface Institute HangzhouZhejiang 311100China Department of Neurology Changxing People’s HospitalChangxingZhejiang 313100China MOE Frontier Science Center for Brain Science and Brain-Machine Integration School of Brain Science and Brain MedicineZhejiang UniversityHangzhouZhejiang 310012China

To the Editor:Spinocerebellar ataxia type 3(SCA3)is the predominant subtype,representing 48-73%of all SCAs in the Chinese population.[1]It primarily manifests as progressive ataxia,characterized by unsteady gait,dysarthria,and limb clumsiness,due to cerebellar and interconnected gray matter ***,extracerebellar features such as extrapyramidal and oculomotor abnormalities,and spasticity,often remain *** complexity can lead to misdiagnosis,underscoring the necessity for a more comprehensive understanding of both cerebellar and extracerebellar symptoms.

关键词： representing cerebellar predominant

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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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