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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Haozheng Wang Hao Jia Zhe Sun Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin China Department of Engineering University of Vic-Central University of Catalonia Vic Spain Faculty of Health Data Science Juntendo University Urayasu Chiba Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Upper limb amputees face significant challenges in their daily lives due to the loss of hand or arm functionality. Researchers have developed upper limb prostheses to restore normal hand movements for them. Most hand movement recognition systems of prostheses use electromyography (EMG) as the input signal source, but ignore the interrelationship with electroencephalography (EEG), which may contain valuable movement-related information as well. In order to enhance the accuracy of hand movement classification, we proposed a hand movement recognition system based on a one-dimensional convolutional neural network (1D-CNN) that combines EEG and EMG as the input signal sources to increase the quantity of accessible information. In this work, we collected the EEG and EMG of five subjects during the hand movements and used a 1D-CNN based model to classify the preprocessed signals. The average accuracy of using EEG-EMG fusion is 96.59±2.63%, significantly higher than 74.99±8.24% of using single EEG and 90.31±7.16% of using single EMG. Then, we applied the model trained by offline experiment for online recognition, and controlled the Pepper robot to complete the corresponding hand movements. The average accuracy of online recognition can reach 93.00±4.85% by using majority voting method. The results indicate that the method of EEG-EMG fusion can effectively enhance the performance of hand movement recognition system, which promote the development of upper limb prostheses and contribute to the rehabilitation of upper limb amputees.

关键词： Hands Accuracy Muscles brain modeling Fatigue Electromyography Electroencephalography Real-time systems Convolutional neural networks Intelligent robots

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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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Generative Adversarial Networks for Electroencephalogram Signal Analysis: A Mini Review

Generative Adversarial Networks for Electroencephalogram Sig...

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International Winter Workshop on brain-computer interface (BCI)

作者： Junkongshuai Wang Wei Mu Aiping Wang Lu Wang Jiaguan Han Pengchao Wang Lan Niu Jianxiong Bin 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 Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

brain-computer interface (BCI) technology based on electroencephalography (EEG) signals is growing rapidly and attracting widespread attention. However, due to the EEG acquisition methods, the quality and quantity of EEG signals are not able to be guaranteed. To alleviate the problems caused by the lack of data, in this paper, we introduce the applications of EEG signals using generative adversarial networks (GANs) which have shown great performance in image data augmentation and other time series data and then discuss their performance.

关键词： Time series analysis Generative adversarial networks Electroencephalography brain-computer interfaces Signal analysis

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A discussion of statistical criteria for assessing awareness with SMR BCI after brain injury

A discussion of statistical criteria for assessing awareness...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Idorenyin Amaunam Christoph Schneider Marina Lopes da Silva Jane Jöhr Karin Diserens Serafeim Perdikis Brain-Computer Interface and Neural Engineering Laboratory School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Department of Radiology Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Clinical Neurosciences Acute Neurorehabilitation Unit Division of Neurology Centre Hospitalier Universitaire Vaudois Lausanne Switzerland

This work discusses the implications of selecting particular statistical metrics and thresholds as criteria to diagnose awareness through brain-computer interface (BCI) technology in patients with Disorders of Consciousness (DOC). We report a first analysis of a novel dataset collected to investigate whether a motor attempt electroencephalography (EEG) paradigm coupled with Functional Electrical Stimulation (FES) can detect command following and, therefore, signs of conscious awareness in DOC. We assessed 22 DOC patients admitted to the acute rehabilitation unit after a brain lesion over one or more sessions. We extracted EEG sensorimotor rhythms and performed a standard open-loop BCI pipeline evaluation, classifying motor attempt against resting-state trials. We validate this approach by correlating classification accuracy with the established clinical scale Coma Recovery Scale Revised. We employ a machine learning (ML)-inspired diagnostic criterion based on confidence intervals over chance-level classification accuracy and show that it yields more conservative and, arguably, reliable inference of Cognitive Motor Dissociation (CMD) by means of command-following, neuroimaging-based tools, compared to diagnoses based on clinical assessments or criteria examining the statistical significance of brain features across different mental states.

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BCI Assisted Motor Function Improvement and Recovery in Children with Cerebral Palsy

BCI Assisted Motor Function Improvement and Recovery in Chil...

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2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2023

作者： Bobrov, Pavel Anastasia, Sokolova Lukyanov, Evgeniy Lyukmanov, Roman Olga, Lajsheva Pirogov Russian National Research Medical University Department. of Neuro-computer Interfaces Moscow Russia Institute of Higher Nervous Activivty and Neurophysiology Ras Laboratory of Mathematical Neurobiology of Learning Moscow Russia Russian Children's Clinical Hospital of Pirogov Russian National Research Medical University Rehabilitation Department Moscow Russia Brain-computer Interface Group Research Center of Neurology Moscow Russia Pirogov Russian National Research Medical University Department of Rehabilitation Sports Medicine and Physical Culture Moscow Russia

ISBN: (纸本)9798350307979

The paper reports the results of BCI-controlled exoskeleton trials for 88 children and adolescents diagnosed with cerebral palsy and having unilateral paresis or hemiplegia. Significant improvement of motor function according to ARAT Fugl-Meyer, Jebsen-Taylor, and Box and Blocks test were observed, as well as increase in muscle force according to MRCSS. BCI performance indices show that the patients with cerebral palsy were capable of controlling the exoskeleton with accuracy comparable to post-stroke patients. Further analysis indicates that higher gain of the clinical scales is associated with higher F-scores for paretic hand and lower F-scores for intact hand movement imagery classification. © 2023 IEEE.

关键词： brain computer interface

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Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

Enhancing Motor Imagery EEG Signal Classification with Simpl...

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International Winter Workshop on brain-computer interface (BCI)

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

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

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Clinical efficacy and safety of vortioxetine as an adjuvant drug for patients with bipolar depression

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2025年第1期26卷 26-38页

作者： Chunxiao DAI Yaoyang FU Xuanwei LI Meihua LIN Yinbo LI Xiao LI Keke HUANG Chengcheng ZHOU Jian XIE Qingwei ZHAO Shaohua HU Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Department of Clinical Psychology Hangzhou First People’s HospitalHangzhou 310006China Department of Clinical Pharmacy the First Affiliated HospitalZhejiang University School of MedicineHangzhou 310003China Fuyang Third People’s Hospital Hangzhou 311402China Nanhu Brain-Computer Interface Institute Hangzhou 311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou 310003China School of Brain Science and Brain Medicine and MOE Frontier Science Center for Brain Science and Brain-Machine IntegrationZhejiang University School of MedicineHangzhou 310012China Brain Research Institute of Zhejiang University Hangzhou 310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhou 310003China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou 310003China

Objective:Whether vortioxetine has a utility as an adjuvant drug in the treatment of bipolar depression remains *** study aimed to validate the efficacy and safety of vortioxetine in bipolar ***:Patients with bipolarⅡdepression were enrolled in this prospective,two-center,randomized,12-week pilot *** main indicator for assessing treatment effectiveness was a Montgomery-Asberg Depression Rating Scale(MADRS)of≥50%.All eligible patients initially received four weeks of lurasidone *** who responded well continued to receive this kind of ***,no-response patients were randomly assigned to either valproate or vortioxetine treatment for eight *** comprehensively comparing the results of MADRS over a period of 4-12 weeks,a systematic analysis was conducted to determine whether vortioxetine could be used as an adjuvant drug for treating bipolar ***:Thirty-seven patients responded to lurasidone monotherapy,and 60 patients were randomly assigned to the valproate or vortioxetine group for eight *** two weeks of combined valproate or vortioxetine treatment,the MADRS score in the vortioxetine group was significantly lower than that in the valproate *** was no difference in the MADRS scores between the two groups at 8 and 12 *** incidence of side effects did not significantly differ between the valproate and vortioxetine ***,three patients in the vortioxetine group appeared to switch to mania or ***:This study suggested that lurasidone combination with vortioxetine might have potential benefits to bipolar II depression in the early stage,while disease progression should be monitored closely for the risk of switching to mania.

关键词： BipolarⅡdepression Lurasidone Vortioxetine Combination

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Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

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eLight 2022年第1期2卷 132-142页

作者： Qian Ma Wei Gao Qiang Xiao Lingsong Ding Tianyi Gao Yajun Zhou Xinxin Gao Tao Yan Che Liu Ze Gu Xianghong Kong Qammer HAbbasi Lianlin Li Cheng‑Wei Qiu Yuanqing Li Tie Jun Cui Institute of Electromagnetic Space Southeast UniversityNanjing 210096China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China State Key Laboratory of Millimeter Wave Southeast UniversityNanjing 210096China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou 510330China Research Center for Brain-Computer Interface Pazhou LabGuangzhou 510330China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore University of Glasgow James Watt School of EngineeringGlasgow G128QQUK State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsPeking UniversityBeijing 100871China

brain-computer interfaces(BCIs),invasive or non-invasive,have projected unparalleled vision and promise for assisting patients in need to better their interaction with the *** by the BCI-based rehabilitation technologies for nerve-system impairments and amputation,we propose an electromagnetic brain-computer-metasurface(EBCM)paradigm,regulated by human’s cognition by brain signals directly and *** experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively,which can be further processed and transported by an information metasurface in automated and wireless *** wireless communications of the human minds are performed between two EBCM operators with accurate text ***,several other proof-of-concept mind-control schemes are presented using the same EBCM platform,exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning,wave modulations,and pattern encoding.

关键词： communication computer surface

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Light Radiation Simulation of Micro-LED Array for the Optical neural interface Targeted Cerebral Cortex in Rhesus Monkey

Light Radiation Simulation of Micro-LED Array for the Optica...

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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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