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International Winter Workshop on brain-computer interface (BCI)

作者： Lusheng Liu Wenlei Zhang Bo Wen Minjie Wang Aiping Wang Jing Zhang Yuan Zhang Song Le Haifeng Jiang Rongwei Zhai 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 Shanghai Mental Health Center Shanghai China Jiao Tong University School of Medicine Shanghai China Lingang Laboratory Shanghai China

The advanced technology of optogenetics has greatly promoted the development of neuroscience. To further optimize the optical neural interface, understanding the propagation process of light in neural tissues is essential for neuroscience research on the application of LED in optogenetics. In this work, we use TracePro software to build geometric models and simulate LED devices. The Rhesus monkey brain model is established to simulate the neural tissue to analyze the irradiance distribution at a specific location, to obtain the effective lighting range. In addition, we analyzed the influencing factors of the illumination range of the implanted light source device. We believe that the distance between devices should be $1.1 \sim1.2$ times of the light range of the simulation result when the devices fit the brain.

关键词： Neuroscience Shape Simulation Lighting Implants Light emitting diodes Optogenetics

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Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging

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Personality Neuroscience 2023年第1期6卷 e3-e3页

作者： Montag, Christian Klugah-Brown, Benjamin Zhou, Xinqi Wernicke, Jennifer Liu, Congcong Kou, Juan Chen, Yuanshu Haas, Brian W. Becker, Benjamin Department of Molecular Psychology Institute of Psychology and Education Ulm University Ulm Germany Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology Chengdu China Institute of Brain and Psychological Sciences Sichuan Normal University Chengdu China Department of Psychology Xinxiang Medical University Henan China Department of Psychology University of Georgia Athens GA United States

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-Thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be hardwired to the brain's neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Artificial intelligence MRI Trust

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Distributed functional connectivity impairment in schizophrenia: A multi-site study 2

Distributed functional connectivity impairment in schizophre...

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2nd IET International Conference on Biomedical Image and Signal Processing, ICBISP 2017

作者： Yang, Yong Cui, Yue Xu, Kaibing Liu, Bing Song, Ming Chen, Jun Wang, Huiling Chen, Yunchun Guo, Hua Li, Peng Lu, Lin Lv, Luxian Wan, Ping Wang, Huaning Yan, Hao Yan, Jun Zhang, Hongxing Zhang, Dai Jiang, Tianzi Brainnetome Center Institute of Automation Chinese Academy of Sciences Beijing100190 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Radiology Renmin Hospital Wuhan University Wuhan430060 China Department of Psychiatry Renmin Hospital Wuhan University Wuhan430060 China Department of Psychiatry Xijing Hospital Fourth Military Medical University Xi'an710032 China Zhumadian Psychiatric Hospital Zhumadian463000 China Peking University Sixth Hospital Institute of Mental Health Beijing100191 China Key Laboratory of Mental Health Ministry of Health Peking University Beijing100191 China Department of Psychiatry Henan Mental Hospital Second Affiliated Hospital of Xinxiang Medical University Xinxiang453002 China Henan Key Lab of Biological Psychiatry Xinxiang Medical University Xinxiang453002 China Department of Psychology Xinxiang Medical University Xinxiang453002 China Center for Life Sciences PKU-IDG McGovern Institute for Brain Research Peking University Beijing100871 China Queensland Brain Institute University of Queensland BrisbaneQLD4072 Australia CAS Center for Excellence in Brain Science and Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781785613180

Schizophrenia has been considered as a dysconneciton syndrome, which means the disintegration, or over interaction between brain regions may underlie the pathophysiology of this disease. Noninvasive techniques like functional magnetic resonance imaging (fMRI) were utilized to test this hypothesis. However, there is no consensus on which brain areas and which functional network is related with it, mostly due to the small sample size of previous studies. Supervised machine learning techniques are able to examine fMRI connectivity data in a multivariate manner and extract features predictive of group membership. This technique requires large sample sizes and results from small sample study may not generalize well. By applying a multi-task classification framework to large size multi-site schizophrenia resting functional MRI (rsMRI) dataset, we were able to find consistent and robust features. We observed that schizophrenia patients had widespread deficits in the brain. The most informative and robustly selected functional connectivity (FC) features were between and within functional networks such as the default mode network (DMN), the fronto-parietal control network (FPN), the subcortical network, and the cingulo-opercular task control network (CON). Our finding validated the dysconnection hypothesis of schizophrenia and shed light on the details of the impaired functional connectivity.

关键词： Magnetic resonance imaging

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Diffusion Adaptive Filtering Algorithm Based on the Fair Cost Function

Research Square

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Research Square 2021年

作者： Guan, Sihai Cheng, Qing Zhao, Yong Biswal, Bharat College of Electronic and Information Southwest Minzu University China Key Laboratory of Electronic and Information Engineering State Ethnic Affairs Commission China Sichuan Vocational College of Finance and Economics Chengdu China School of Mechanical and Power Engineering Henan Polytechnic University Jiaozuo China The Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for Neuroinformation Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China NewarkNJ United States

To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the RDLMS, DNLMM, DGCLD, and DPLMS algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments. © 2021, CC BY.

关键词： Cost functions

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A Reversible Ratiometric pH-sensitive Nanomachine Based on DNA Duplex-triplex Transition

A Reversible Ratiometric pH-sensitive Nanomachine Based on D...

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中国化学会第十届全国发光分析学术研讨会

作者： Mengqi Yang Haipeng Liu Wen Yang Xiaoling Zhang Weihong Tan Center for Research at Bio/nano Interface Department of Chemistry and Department of Physiology and Functional Genomics Shands Cancer Center UF Genetics Institute and McKnight Brain InstituteUniversity of Florida Gainesville Florida 32611-7200 USA Key laboratory of Cluster Science of Ministr of Education School of Chemistry Beijing Institute of Technology Beijing 100081 P.R.China Center for Research at Bio/nano Interface Department of Chemistry and Department of Physiology and Functional Genomics Shands Cancer Center UF Genetics Institute and McKnight Brain InstituteUniversity of Florida Gainesville Florida 32611-7200 USA Key laboratory of Cluster Science of Ministr of Education School of Chemistry Beijing Institute of Technology Beijing 100081 P.R.China

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Oracle Character Recognition by Nearest Neighbor Classification with Deep Metric Learning

Oracle Character Recognition by Nearest Neighbor Classificat...

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International Conference on Document Analysis and Recognition

作者： Yi-Kang Zhang Heng Zhang Yong-Ge Liu Qing Yang Cheng-Lin Liu National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing P.R. China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing P.R. China School of Computer & Information Engineering Anyang Normal University Henan P.R. China Ministry of Education Key Laboratory of Oracle Bone Inscriptions Information Processing Henan P.R. China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing P.R. China

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU.

关键词： Prototypes Character recognition Measurement Artificial neural networks Training Acceleration Network architecture

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Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition

arXiv

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arXiv 2020年

作者： Wu, Xun Zheng, Wei-Long Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China Clinical Data Animation Center Department of Neurology Massachusetts General Hospital Harvard Medical School 55 Fruit Street BostonMA United States

Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP. The strength feature achieves the best classification performance and outperforms the state-of-the-art differential entropy feature based on single-channel analysis for the EEG signals. The experimental results reveal that distinct functional connectivity patterns are exhibited for the five emotions of disgust, fear, sadness, happiness, and neutrality. Furthermore, we construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals using deep canonical correlation analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42% on the SEED dataset, 84.51 ± 5.11% on the SEED-V dataset, and 85.34 ± 2.90% and 86.61 ± 3.76% for arousal and valence on the DEAP dataset, respectively. The results demonstrate the complementary representation properties of the EEG functional connectivity network features with eye movement data. In addition, we find that the brain networks constructed with fewer channels, i.e., 18 channels, achieve comparable performance with that of the 62-channel network with respect to multimodal emotion recognition and enable easier setups for BCI systems in real scenarios. Cop

关键词： Electrophysiology

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A ratiometric fluorescent probe for imaging of palladium

A ratiometric fluorescent probe for imaging of palladium

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中国化学会第十届全国发光分析学术研讨会

作者： Yamin Li Xiaoling Zhang Baocun Zhu Hongying Jia Juan Xue Weihong Tan Key Laboratory of Cluster Science of Ministry of Education Department of chemistry School of Science Beijing Institute of Technology Beijing People''s Republic of China Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of Chemistry Chinese Academy of Sciences Beijing 100190People''s Republic of China Department of Chemistry and Department of Physiology and Functional Genomics Shands Cancer Center and Center for Research at the Bio/nano Interface UF Genetics Institute and McKnight Brain Institute University of Florida Gainesville Florida 32611-7200 USA

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Genetics Blood-brain barrier–penetrating single CRISPR-Cas9 nanocapsules for effective and safe glioblastoma gene therapy

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science Advances 2022年第16期8卷

作者： Zou, Yan Sun, Xinhong Yang, Qingshan Zheng, Meng Shimoni, Olga Ruan, Weimin Wang, Yibin Zhang, Dongya Yin, Jinlong Huang, Xiangang Tao, Wei Park, Jong Bae Liang, Xing-Jie Leong, Kam W. Shi, Bingyang Henan-Macquarie Uni Joint Centre for Biomedical Innovation Academy for Advanced Interdisciplinary Studies Henan Key Laboratory of Brain Targeted Bio-nanomedicine School of Life Sciences Henan University Henan Kaifeng475004 China Macquarie Medical School Faculty of Medicine Health and Human Sciences Macquarie University SydneyNSW2109 Australia Faculty of Science University of Technology Sydney 15 Broadway Ultimo SydneyNSW2007 Australia Center for Nanomedicine Department of Anesthesiology Harvard Medical School 25 Shattuck St. BostonMA02115 United States Department of Cancer Biomedical Science Graduate School of Cancer Science and Policy National Cancer Center Goyang10408 Korea Republic of Center for Excellence in Nanoscience CAS Key Laboratory for Biological Effects of Nano-materials and Nanosafety National Center for Nanoscience and Technology Chinese Academy of Sciences Beijing100190 China Department of Biomedical Engineering Columbia University New YorkNY10032 United States

We designed a unique nanocapsule for efficient single CRISPR-Cas9 capsuling, noninvasive brain delivery and tumor cell targeting, demonstrating an effective and safe strategy for glioblastoma gene therapy. Our CRISPR-Cas9 nanocapsules can be simply fabricated by encapsulating the single Cas9/sgRNA complex within a glutathione-sensitive polymer shell incorporating a dual-action ligand that facilitates BBB penetration, tumor cell targeting, and Cas9/sgRNA selective release. Our encapsulating nanocapsules evidenced promising glioblastoma tissue targeting that led to high PLK1 gene editing efficiency in a brain tumor (up to 38.1%) with negligible (less than 0.5%) off-target gene editing in high-risk tissues. Treatment with nanocapsules extended median survival time (68 days versus 24 days in nonfunctional sgRNA-treated mice). Our new CRISPR-Cas9 delivery system thus addresses various delivery challenges to demonstrate safe and tumor-specific delivery of gene editing Cas9 ribonucleoprotein for improved glioblastoma treatment that may potentially be therapeutically useful in other brain diseases. Copyright © 2022 The Authors, some rights reserved.

关键词： Nanocapsules

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CEBRA Method: Decoding brain Activity for Advanced brain-computer interface technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, brain-computer interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines brain Accuracy Stroke (medical condition) Feature extraction Motors brain-computer interfaces Neurorehabilitation Decoding Paralysis

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