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Proceedings. 2005 First International Conference on Neural Interface and Control, 2005.最新文献

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Brain-computer interface based on the high-frequency steady-state visual evoked potential 基于高频稳态视觉诱发电位的脑机接口
Wang Yijun, W. Ruiping, G. Xiaorong, G. Shangkai
Low-frequency steady-state visual evoked potentials (SSVEPs) are used as the input signal in the present SSVEP-based brain-computer interface (BCI). This prototype system has a high information transfer rate. On the other hand, it has some limitations including visual fatigue, false positive, and some possibility of causing a seizure. These drawbacks can be largely eliminated when using high-frequency stimulations. In this paper, we study the amplitude versus stimulation frequency response of SSVEPs. The signal-to-noise ratio versus frequency curve suggests that the high-frequency SSVEP (>20Hz) could help to construct a practical BCI system.
基于低频稳态视觉诱发电位(ssvep)的脑机接口(BCI)采用低频稳态视觉诱发电位作为输入信号。该原型系统具有较高的信息传输率。另一方面,它也有一些局限性,包括视觉疲劳,假阳性,以及引起癫痫发作的可能性。当使用高频刺激时,这些缺点可以在很大程度上消除。在本文中,我们研究了ssvep的振幅与刺激频率响应。信噪比与频率曲线表明,高频SSVEP (> ~ 20Hz)可以帮助构建一个实用的脑机接口系统。
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引用次数: 91
Experiment study of the relation between motion complexity and event-related desynchronization/synchronization 运动复杂度与事件相关非同步/同步关系的实验研究
Chen Qiang, Peng Hu, Feng Huanqing
To find a new method to get more classifiable EEG patterns in event-related desynchronization/synchronization based brain-computer interface, we designed a experiment to study the relation between finger motion complexity and the energy features of EEG. Data analysis show that around primary motor cortex, energy of finger motion under different complexity shows notable distinction in /spl beta/ band.
为了在基于事件相关非同步/同步的脑机接口中寻找一种新的可分类的脑电模式,我们设计了一个实验来研究手指运动复杂度与脑电能量特征之间的关系。数据分析表明,在初级运动皮层周围,不同复杂程度下手指运动能量在/spl β /波段上存在显著差异。
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引用次数: 10
Using the correlation dimension for estimation of the complexity of visual evoked potential signal 利用相关维数估计视觉诱发电位信号的复杂性
Dang Yanjun, Lin Jiarui, Yang Zhong-le, Shu Weiguang
The aim of this study is to detect nonlinear dynamic properties in evoked potential (EP) with correlation dimension (CD). We also want to identify if CD could be used as a discriminating statistic of spontaneous EEG and EP. The CD of EEG before and after stimulations are studied. The CD of EP in single trial form and multitrials average form are also studied. Results from this study show that the correlation dimensions of evoked potential is lower than spontaneous EEG. EP signals have less complicated properties than spontaneous EEG.
本研究的目的是利用相关维数(CD)检测诱发电位的非线性动态特性。我们还想确定CD是否可以作为自发性脑电图和EP的鉴别统计。研究了刺激前后的脑电图CD。研究了单次试炼和多次试炼平均态下EP的CD。结果表明,诱发电位的相关维度低于自发性脑电。EP信号具有比自发脑电图更简单的特征。
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引用次数: 1
The closed-loop human-computer interface: active information acquisition for vision-brain-hand to computer (VBH-C) interaction based on force tablet 闭环人机界面:基于力板的视觉-脑-手-机(vvb -c)交互主动信息获取
Wu Zhong-cheng, Kang Le, Shen Fei, Fang Bin
As more attention on human beings, developing an effective communication interface connecting the human brain to computer becomes an active research area in recent years. Until now, no efficient device can record the entire procedure of intrinsic human behavior information, including acquiring, transmitting, processing and outputting. In this paper, we offer a system which consists of computer and F-tablet, the later can acquire the kinematics and kinetics information of human handwriting. Joined with human beings, a vision-brain-hand to computer (VBH-C) interaction system is presented. In this human-in-the-loop-testing system, human beings acquire image, voice or text from computer by eyes or ears then write them down. As a closed-loop feedback portion, the designed F-Tablet acquires strokes of pen-up and pen-down, velocity and acceleration of pen-tip, three dimension forces of pen-plate contacting point and shape of character etc. The core part of the system, named as F-Tablet, is introduced which is able to acquire the trajectory and three-axis forces directly and simultaneously of pen-tip. A simple test will be done to judge the movements and forces controlling ability of different ages with the help of the system. Comparing with other brain-computer interface (BCI), VBH-C interface can offer more information, especially as a closed-loop and information feedback, it is easier for human decision pattern identification.
随着人们对人的重视,开发一种连接人脑与计算机的有效通信接口成为近年来的一个活跃研究领域。到目前为止,还没有一种高效的设备能够记录人类固有行为信息的整个过程,包括获取、传输、处理和输出。本文提出了一个由计算机和平板电脑组成的系统,平板电脑可以获取人体笔迹的运动学和动力学信息。结合人类,提出了一种视觉-脑-手-机交互系统。在这个“人在循环”测试系统中,人类通过眼睛或耳朵从电脑上获取图像、声音或文本,然后把它们写下来。所设计的F-Tablet作为闭环反馈部分,获取笔尖上下笔划、笔尖速度和加速度、笔板接触点三维力、字符形状等信息。介绍了系统的核心部分F-Tablet,它可以同时直接获取笔尖的轨迹和三轴力。通过一个简单的测试来判断不同年龄的人的运动和力的控制能力。与其他脑机接口(BCI)相比,vvb -c接口可以提供更多的信息,特别是作为闭环和信息反馈,更易于人类决策模式识别。
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引用次数: 4
Simulation and control of epileptic EEG via a nonlinear model on Simulink 基于Simulink的癫痫脑电非线性模型仿真与控制
X. Tian, Z. Xiao
A nonlinear model between brainstem, cortex and thalamus circuits is established on neural population level on Matlab/Simulink. The electric activities in this brain circuits are simulated. The output of this model is the derivatives of postsynaptic potentials from thalamus, which are reflected in EEG in both normal and epileptic EEGs. The epileptic EEGs are simulated via model with a dysfunction between brainstem and cortex, then controlled to normal by adding a perturbation to the cortex or sensory. The amplitude and the correlation dimension of the simulated EEG are used as the index of the dynamic behavior of EEG.
利用Matlab/Simulink在神经种群水平上建立了脑干、皮层和丘脑回路的非线性模型。大脑回路中的电活动被模拟。该模型的输出是丘脑突触后电位的衍生物,在正常和癫痫的脑电图中都反映在脑电图中。通过脑干和皮层之间的功能障碍模型模拟癫痫脑电图,然后通过对皮层或感官进行扰动控制到正常状态。利用模拟脑电信号的幅值和相关维数作为脑电信号动态行为的指标。
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引用次数: 3
A review of fundamental mechanisms and techniques in functional electrical stimulation 功能电刺激的基本机制和技术综述
Xu Qi, He Jiping, Wang Yongji, Xu Tao, Huang Jian
In this paper, we review various electrodes with individual design, address the influences of essential stimulator parameters and internal disturbances on the recruitment characteristics, and describe the control strategies of functional electrical stimulation (FES) musculoskeletal systems. The promise of possible directions for further research on safe and effective FES systems are also discussed.
在本文中,我们回顾了各种不同设计的电极,讨论了基本刺激器参数和内部干扰对招募特性的影响,并描述了功能电刺激(FES)肌肉骨骼系统的控制策略。讨论了安全有效的FES系统进一步研究的可能方向。
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引用次数: 1
Using morphological filters to extract spiky transients in EEG 利用形态学滤波器提取脑电瞬变信号
X. Luo, C. Peng, X.M. Guo
In this paper, we use four basic morphological operators to construct a bandpass filter, and also investigate the construct method of the filter in detail. Furthermore, we give the algorithm flowchart of this filter. The result of experiment shows that this morphological filter can be applied to eliminate noise from EEG signal by two different length structure elements and it has advanced property that can remove noise without damage signal useful information. Using this algorithm, we can filter noise and extract spiky transients in EEG effectively.
本文利用四种基本形态学算子构造了一个带通滤波器,并详细研究了该滤波器的构造方法。并给出了该滤波器的算法流程图。实验结果表明,该形态学滤波器可以通过两种不同长度的结构元素去除脑电信号中的噪声,并且具有去除噪声而不破坏信号有用信息的先进性。利用该算法可以有效地滤除脑电信号中的噪声,提取脑电信号中的尖波瞬态。
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引用次数: 0
Mental tasks classification and their EEG structures analysis by using the growing hierarchical self-organizing map 基于增长层次自组织图的心理任务分类及其脑电结构分析
Liu Hailong, Wang Jue, Z. Chong-xun
The unsupervised method of growing hierarchical self-organizing map (GHSOM) was used to perform mental tasks classification. The GHSOM is an adaptive artificial neural network model with hierarchical architecture that is able to detect the hierarchical structure of data. The results indicate that GHSOM provides more detailed clustering information than SOM, and gives visual information about the separability of mental tasks in an intuitive way. The average classification accuracy across 130 task pairs by using GHSOM was up to 96.7%.
采用无监督分层自组织图生长方法(GHSOM)进行心理任务分类。GHSOM是一种具有层次结构的自适应人工神经网络模型,能够检测数据的层次结构。结果表明,GHSOM提供了比SOM更详细的聚类信息,并以直观的方式提供了关于心理任务可分性的视觉信息。在130个任务对中,GHSOM的平均分类准确率高达96.7%。
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引用次数: 10
A new method to monitor depth of anesthesia based on the autocorrelation EEG signals 基于自相关脑电图信号的麻醉深度监测新方法
Zhang Lianyi, Z. Chong-xun
To have a safe, noninvasive, reliable and economic anesthetic depth indicator, the change of the a rhythm of electroencephalogram (EEG) signal on autocorrelation property during general intravenous anesthesia is investigated based on the effects of general anesthetics on the a rhythm of EEG in prefrontal cortex area. To synthesize the effects of correlated behavior, the contamination of muscle artifact is not removed from the EEG data. The autocorrelation analysis shows: 1) The EEG signals in prefrontal cortex area on autocorrelation are sensitive to different anesthesia depths during general anesthesia. The difference of autocorrelation trace from awareness to anesthesia is obvious. The change of autocorrelation trace is consistent with the anesthesia process; 2) The changes of autocorrelation in FP1-Cz channel and FP2-Cz channel with time are almost synchronous during general anesthesia. This means that 1-channel- recordings from prefrontal cortex are sufficient to monitor depth of anesthesia; 3) The value of autocorrelation in anesthesia fluctuates within a small range and is small than 20. This means that the value of autocorrelation is stable in anesthesia; 4) The differences of the range that the value of autocorrelation fluctuates in anesthesia present individual differences in a way. Being calculation simple, single channel and the transition of autocorrelation trace from awareness to anesthesia obvious, this technique may be ease to use, low running cost and can be applied in real time. Autocorrelation may provide a new method to monitor depth of anesthesia in clinic.
为了获得一种安全、无创、可靠、经济的麻醉深度指标,本文基于全麻对前额叶皮质区脑电图a节律的影响,研究了全身静脉麻醉时脑电图信号a节律对自相关特性的变化。为了综合相关行为的影响,不去除脑电数据中肌肉伪影的污染。自相关分析表明:1)在全身麻醉时,具有自相关的前额叶皮质区脑电信号对不同麻醉深度敏感。从清醒到麻醉的自相关轨迹差异明显。自相关迹线的变化与麻醉过程一致;2)全身麻醉时,FP1-Cz通道和FP2-Cz通道自相关随时间的变化几乎是同步的。这意味着来自前额皮质的单通道记录足以监测麻醉的深度;3)麻醉中自相关值波动范围较小,小于20。说明麻醉状态下自相关值是稳定的;4)麻醉中自相关值波动范围的差异在一定程度上呈现个体差异。该技术计算简单,通道单一,自相关迹线从意识到麻醉的过渡明显,易于使用,运行成本低,可实时应用。自相关可为临床麻醉深度监测提供新的方法。
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引用次数: 5
Study of epileptic rat's EEG using bispectrum analysis 应用双谱分析研究癫痫大鼠脑电图
Wu Hongyi, Xia Yang, Lai Yongxiu, L. Yansu, Y. Dezhong
In order to obtain a sensitive parameter to discriminate the different stages of epilepsy, we studied pilocarpine-induced epileptic rat's ECoG and EHG by bispectrum analysis method based on the assumption that EEG is nonGaussian and nonlinear signal. In this paper, we proposed a model of EEG signals according to the parameter model stimulated by nonGaussian white noise to estimate the bispectrum of EEG. The results showed that the bispectrum analysis is sensitive to the epileptic and nonepileptic EEG. From these results, the quantified parameters presenting the features of epileptic EEG can be found, which could be new evidences to clinical monitoring and predicting of seizure.
在假定脑电图是非高斯非线性信号的基础上,采用双谱分析方法对匹罗卡品诱发的癫痫大鼠脑电图和脑电图进行了研究,以期获得一个判别癫痫不同阶段的敏感参数。本文提出了一种基于非高斯白噪声刺激参数模型的脑电信号模型,用于估计脑电信号的双谱。结果表明,双谱分析对癫痫性和非癫痫性脑电图具有较好的敏感性。从这些结果中可以找到表征癫痫性脑电图特征的量化参数,为临床监测和预测癫痫发作提供新的依据。
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引用次数: 5
期刊
Proceedings. 2005 First International Conference on Neural Interface and Control, 2005.
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