检索结果-内蒙古大学图书馆

Implantable Neural Microelectrodes: How to Reduce Immune Response

ACS BIOMATERIALS SCIENCE & ENGINEERING 2024年第5期10卷 2762-2783页

作者： Xiang, Ying Zhao, Yuewu Cheng, Tingting Sun, Shengkai Wang, Jine Pei, Renjun Univ Sci & Technol China USTC Sch Nanotech & Nanobion Hefei 230026 Peoples R China Chinese Acad Sci Suzhou Inst Nanotech & Nanobion CAS Key Lab Nanobio Interface Suzhou 215123 Peoples R China Jiangxi Inst Nanotechnol Nanchang 330200 Peoples R China Dezhou Univ Coll Med & Nursing Shandong Prov Engn Lab Novel Pharmaceut Excipients Dezhou 253023 Peoples R China

Implantable neural microelectrodes exhibit the great ability to accurately capture the electrophysiological signals from individual neurons with exceptional submillisecond precision, holding tremendous potential for advancing brain science research, as well as offering promising avenues for neurological disease therapy. Although significant advancements have been made in the channel and density of implantable neural microelectrodes, challenges persist in extending the stable recording duration of these microelectrodes. The enduring stability of implanted electrode signals is primarily influenced by the chronic immune response triggered by the slight movement of the electrode within the neural tissue. The intensity of this immune response increases with a higher bending stiffness of the electrode. This Review thoroughly analyzes the sequential reactions evoked by implanted electrodes in the brain and highlights strategies aimed at mitigating chronic immune responses. Minimizing immune response mainly includes designing the microelectrode structure, selecting flexible materials, surface modification, and controlling drug release. The purpose of this paper is to provide valuable references and ideas for reducing the immune response of implantable neural microelectrodes and stimulate their further exploration in the field of brain science.

关键词： implantable neural microelectrodes brain-computerinterface immune response

来源：评论

学校读者我要写书评

暂无评论

Cross-subject fusion based on time-weighting canonical correlation analysis in SSVEP-BCIs

引用

MEASUREMENT 2022年 199卷

作者： Sun, Ying Ding, Wenzheng Liu, Xiaolin Zheng, Dezhi Chen, Xinlei Hui, Qianxin Na, Rui Wang, Shuai Fan, Shangchun Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Sch Res Inst Frontier Sci Beijing 100191 Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data based Precis Med Beijing 100191 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Cheng Lab Shenzhen 518038 Peoples R China

brain-computer interface technology provides new possibilities for medical rehabilitation and human- computer interaction. The steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) is the preferred solution for controlling equipment because of its ease of operation, low training requirements, and high information transfer rate (ITR). However, the low recognition accuracy of SSVEP in short time limits its further improvement in ITR. To address this issue, this paper proposes time-weighting canonical correlation analysis (TWCCA), a time-domain enhancement CCA-based method to improve the recognition accuracy of SSVEP in short recognition time. The TWCCA method evaluates the SSVEP components of EEG signals at different time periods and performs a time-dimension weighted CCA on the original signal. The method integrates the features of all stimulus targets based on CCA and is insensitive to the specific number of targets, so it does not require a large amount of calibration data. To further shorten the calibration time for a certain user, this paper also proposes a cross-subject fusion method to calibrate the parameters of the target user using the acquired data from other users. The effectiveness of the proposed method is evaluated using EEG signals from the occipital and ear regions. The experiment results show that the TWCCA method significantly improves the average recognition accuracy by 3.86% and the cross-subject fusion method shortens the calibration time by 78.5% without compromising recognition accuracy. The proposed combination of cross-subject fusion method and TWCCA (CF-TWCCA) increases recognition accuracy by 13.16% on average (p < 0.001).

关键词： brain-computerinterface Steady-statevisualevokedpotentials Cross-subject Canonicalcorrelationanalysis Time-weighting

来源：评论

学校读者我要写书评

暂无评论

Different Set Domain Adaptation for brain-Computer Interfaces: A Label Alignment Approach

引用

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2020年第5期28卷 1091-1108页

作者： He, He Wu, Dongrui Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Domain adaptation, which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most domain adaptation approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers different set domain adaptation for BCIs, i.e., the source and target domains have different label spaces. We introduce a practical setting of different label sets for BCIs, and propose a novel label alignment (LA) approach to align the source label space with the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject;2) LA can be used as a preprocessing step before different feature extraction and classification algorithms;and, 3) LA can be integrated with other domain adaptation approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.

关键词： Covariance matrices Electroencephalography Manifolds Feature extraction Calibration Silicon Pipelines brain-computerinterface EG label alignment Riemannian geometry domain adaptation transfer learning

来源：评论

学校读者我要写书评

暂无评论

Modulation of Cortical Excitability with BCI for Stroke Rehabilitation 7

Modulation of Cortical Excitability with BCI for Stroke Reha...

引用

7th International Winter Conference on brain-Computer Interface (BCI)

作者： Mrachacz-Kersting, Natalie Farina, Dario Aalborg Univ Fac Med Dept Hlth Sci & Technol Aalborg Denmark Imperial Coll London Dept Bioengn London England

ISBN: (纸本)9781538681169

Here we present the possibility of inducing significant neuroplasticity as assessed by non-invasive transcranial magnetic stimulation (TMS) using a unique brain-Computer Interface (BCI) build on known mechanisms of memory and learning. This BCI associates in time the cortical signals generated when a stroke patient attempts to perform a movement, and the artificial production of that movement. As for healthy participants, both chronic and sub-acute patients show neuroplastic changes following exposure to this BCI, that is accompanied by significant improvements in function as assessed by clinical scales. The relatively short duration of each intervention session, the fact that it does not require user training or residual muscle activity makes this a viable tool for the clinical setting and my pave the way for future BCIs in the clinic.

关键词： component neuroplasticity brain-computerinterface associativity strok Hebb

来源：评论

学校读者我要写书评

暂无评论

Listening to the brain in Real Time-The Chengdu brainwave Music

引用

Journal of Electronic Science and Technology 2009年第1期000卷 34-39页

作者： Dan Wu Yang Liu De-Zhong Yao Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu610054China

Electroencephalogram(EEG) provides a window for the activity of the human *** a novel form of the brain-computer interface(BCI),the online/offline EEG data may be interpreted through its auditory representation which can be considered as a specific tool in EEG monitoring and *** this work,after a comprehensive comparison of the various designs of brainwave music generations,a waveform event mapping system for music display in real time- the Chengdu brainwave Music(CBM) is proposed,which is a special on-line BCI *** CBM,the user datagram protocol(UDP) is adopted to transport EEG data from the recorder to a music *** CBM could possibly be used as an audio feedback tool in BCI,or a monitoring tool in clinic EEG,and a subject specified music therapy method.

关键词： brain-computerinterface electro-encephalogram sonification

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Learning Based on Manifold in BCI

引用

Journal of Electronic Science and Technology 2009年第1期000卷 22-26页

作者： Ji-Ying Zhong Xu Lei De-Zhong Yao Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu610054China

A Laplacian support vector machine(LapSVM) algorithm,a semi-supervised learning based on manifold,is introduced to brain-computer interface(BCI) to raise the classification precision and reduce the subjects’ training *** data are collected from three subjects in a three-task mental imagery *** and transductive SVM(TSVM) are trained with a few labeled samples and a large number of unlabeled *** results confirm that LapSVM has a much better classification than TSVM.

关键词： brain-computerinterface manifoldlearning semi-supervisedlearning supportvectormachine

来源：评论

学校读者我要写书评

暂无评论

Common Spatial Pattern Ensemble Classifier and Its Application in brain-Computer Interface

引用

Journal of Electronic Science and Technology 2009年第1期000卷 17-21页

作者： Xu Lei Ping Yang Peng Xu Tie-Jun Liu De-Zhong Yao Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu610054China

Common spatial pattern(CSP) algorithm is a successful tool in feature estimate of brain-computer interface(BCI).However,CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of *** this paper,we propose a simple yet effective approach,named common spatial pattern ensemble(CSPE) classifier,to improve CSP *** division of recording channels,multiple CSP filters are *** projection,log-operation,and subtraction on the original signal,an ensemble classifier,majority voting,is achieved and outlier contaminations are *** results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

关键词： brain-computerinterface channelselection classifierensemble commonspatialpattern

来源：评论

学校读者我要写书评

暂无评论

fMRI-BCI:a Review

引用

Journal of Electronic Science and Technology 2009年第1期000卷 78-81页

作者： Da-Huan Li Qin Gao Wei-Shuai Lü Hua-Fu Chen Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu610054China

Functional magnetic resonance imaging(fMRI) is a new tool for brain-computer interface(BCI).This paper presents an overview to *** attention is mainly put on the methods of signal acquisition,signal preprocessing,and signal analysis of basic fMRI-BCI *** available softwares and the applications of fMRI-BCI are briefly *** last,we suggest focusing on some technologies to make fMRI-BCI more perfect.

关键词： brain-computerinterface functionalmagneticresonanceimaging real-timesignalprocessing

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Methods in Multi-Class brain-Computer Interface

引用

Journal of Electronic Science and Technology 2009年第1期000卷 12-16页

作者： Ping Yang Xu Lei Tie-Jun Liu Peng Xu De-Zhong Yao Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu610054China

Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface(BCI):support vector machine(SVM) with posteriori probability(PSVM) and Bayesian linear dis-criminant analysis with probabilistic output(PBLDA).A comparative evaluation of these two methods is *** results shows that:1) probabilistic information can improve the performance of BCI for subjects with high kappa coefficient,and 2) PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.

关键词： Bayesianlineardiscriminantanalysis brain-computerinterface kappacoefficient supportvectormachine

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：