通过加强患者的体验以期望获得更好更强的脑电信号。
By enhancing the experience of patients, it's in order to expect better and stronger EEG.
基于脑电的脑—机接口采用了很多种类型的脑电信号。
Several types of EEG signals have been adopted in EEG based BCI.
提出了一种基于特定几种心理作业的脑电信号控制假手的方法。
A control method of the prosthetic hand based on the electroencephalogram (EEG) of several designated mental tasks was proposed.
电路仿真和实物调试都能很好地印证设计目标，能够采集到较为理想的脑电信号。
The simulation of the designed circuit and the debug of objection can be well corroborated and the ideal EEG signal can be obtained in the system.
与特定运动行为相关的脑电信号特征的提取与识别是脑-机接口研究中的关键之一。
Extraction and recognition of features of brain signal related to special movement behavior is one of the keys of BCI technology.
具体设计内容包括：设计具有输入阻抗大、高增益和高共模抑制比的脑电信号调理电路；
The main design work as follow: the former disposal circuits were designed, and the circuit possesses very high input resistance, common-mode rejection and voltage gain;
但是当前的研究多数是针对低维混沌信号，本文对高维混沌特性的脑电信号的预测研究作初步探讨。
However, most existing research results are main about the prediction of the low-dimension chaotic signal, we investigate the prediction of high-dimension chaotic EEG signal in this paper.
本研究提出了一种基于带参考信号的ICA算法的脑电信号眨眼伪差的分离方法，可以得到纯净的脑电信号。
Based on ICA algorithm with reference signals, a method of removing blinking artifacts was proposed in this paper.
在固件设计部分完成了基于USB固件框架的脑电信号采集固件设计、USB传输固件设计及usb硬盘固件设计。
And in the firmware design part, the design of EEG data acquisition firmware, USB communication firmware and USB HDD firmware are carried out.
该平台采用患者在想象运动时的脑电信号作为虚拟人运动的控制信号，从而把想象运动与运动功能恢复训练结合在一起。
This platform applies the patients motor imagery electroencephalogram (EEG) as the control signal in order to combine motor imagery with recovery training of motion function.
然而，自发脑电信号非常弱，噪声大，而且是非平稳信号，因此合适的脑电信号分析研究方法是BCI系统的核心内容。
However, the spontaneous EEG signals are very weak, noisy, and non-stationary signal, so efficient methods of EEG analysis is the core of BCI system.
TimHemmes的手术花了两个小时，他在脑CT中练习想象着手臂的动作，然后得知他大脑中哪部分的电信号集中。
Tim Hemmes’ operation took two hours. He had practiced imagining arm movements inside brain scanners, to see where the electrical signals concentrated.
通过比较上述两种方法检测脑电信号的异同；并结合临床特征及影像学资料，分析CEEG和AEEG的临床价值。
We analysis the clinic value of the CEEG and AEEG by compare the difference of AEEG and CEEG in inspecting electroencephalogram signal, clinic character and video data.
脑电是极为复杂的非周期生物电信号，对脑电的研究始终是现代科学的一大热点。
EEG is a kind of complicated non-periodic bioelectricity signal and its research is still an important field for modern science.
脑电信号是非平稳的随机信号，其中包含了大量的生理和疾病信息，对于医生判断脑部是否有器质性的病变具有重要作用。
The signal of brain activity is a non-stationary random signal including lots of physiology and disease information, which is of important action for doctors to judge pathological changes in brain.
脑电信号（EEG）是人体重要的生物电信号，目前关于脑电信号监测在某些疾病患者治疗过程中的作用得到医疗机构越来越广泛的重视。
The electroencephalogram(EEG) is one of the most important bio-electric signals of the human. Now many people pay much attention to the therapy effects through monitoring the EEG.
脑电信号分析的任务之二就是如何从强背景自发eeg中提取ep信号。
The second task of brain signal analysis is to extract EP signals from spontaneous EEG in strong backgrounds.
本文设计了一种获取脑电信号的采集系统。
A kind of EEG acquisition system is designed in this article.
本文介绍了一种提取脑电信号的自适应卡尔曼滤波算法及其微机处理软件。
This paper introduces an algorithm of adaptive Kalman filter and its microcomputer software for extracting EEG signal.
设计有效的学习算法快速准确地对脑电信号进行连续预测是脑机接口研究的关键之一。
To develop effective learning algorithms for fast and accurate continuous prediction using Electroencephalogram (EEG) signal is a key issue in BrainComputer Interface (BCI).
本文对脑电信号产生的根源、脑电信号源定位的重要性以及脑电信号源定位方法进行了探讨。
In this paper, the source of brain electric signal, the importance of brain electric source localization and the method of brain electric source localization are investigated.
近年来，随着认知科学研究的逐渐升温，将脑电信号分析应用于认知研究成为重要的手段之一。
Recently with the increasing of research for cognitive science, EEG analysis becomes one of the most important the methods in the field of cognitive research.
研究基于脑电信号排列组合熵的运动意识任务自动分类方法。
To present a new application of permutation entropy method for automatic brain consciousness task classification.
脑电信号的功率谱分析结果显示了接触组与对照组在一些指标上有差异。
Many indices of EEG power spectrum were different between the exposed group and control group.
利用ICA方法对实测脑电信号中的心电伪迹和工频噪声进行了消除，成功去除噪声并保留脑电信号的特征不变。
ECG artifact and power noise are successfully removed from the origanal EEG signal with the ICA method with no harm to the details of EEG signals.
一个是基于EMD的时频分布，另一个是基于EMD的非线性能量算子（NEO）方法，其在癫痫脑电信号的处理中都取得了比较好的效果。
One is a time-frequency distribution based EMD, the other is nonlinear energy operator (NEO) based on EMD, and both of them have good results in epileptic EEG signal processing.
脑电信号蕴含着丰富的大脑活动信息，通过对脑电信号的有效分析和处理，可以从中提取出可靠的特征参量来反映不同的脑功能状态。
A wealth of brain information is provided in EEG signals. Some characteristic parameters can be extracted for representing different cerebral function states by careful analyses and processing.
研究了灰色模型在自发脑电特征提取中的应用，同时给出了脑电信号特征提取的总体方案。
The application of gray model in feature extraction of spontaneous EEG signals is studied and the overall scheme of EEG feature extraction is presented.
研究了灰色模型在自发脑电特征提取中的应用，同时给出了脑电信号特征提取的总体方案。
The application of gray model in feature extraction of spontaneous EEG signals is studied and the overall scheme of EEG feature extraction is presented.
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