EEG signal is a kind of very complex non stationary signal.
脑电信号是极其复杂的非平稳信号。
Brain signals are classified into EEG signal and EP signal.
脑电分为自发脑电(EEG)和诱发电位(ep)两种。
This method has been applied to the interference pulse detection in EEG signal.
我们用该方法对实测脑电信号（EEG）中瞬态脉冲干扰进行检测。
The 12-bit ADC in C8051F020 samples the EEG signal amplified by the preamplifier.
脑电信号通过前置放大器放大后，由C8051F 020内部的12位ADC进行采样。
It consists of EEG signal amplifier, data acquisition, DSP module and executive unit.
这部分主要由脑电放大和采集器、DSP模块和执行单元组成。
Based on analyzing the character of EEG signal, we design the AEEG recorder with 18 channels.
本文在对脑电信号进行特征分析的基础上，设计并实现了18导动态脑电图记录盒。
The EEG signal is collected with an electrode, and then is sent to transforming and processing part of the system.
系统设计采用主动电极提取信号，而后将其送入信号的变换与处理部分。
This paper introduces an algorithm of adaptive Kalman filter and its microcomputer software for extracting EEG signal.
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。
KICA with model selection step is applied to the task of removing ECG artifact from the EEG signal and the result shows KICA.
实验结果表明：经模型选择后的KICA能成功分离脑电信号中的心电伪差。
EEG signal is characterized by high-dimension chaotic. The prediction of chaotic signal is an importance area of chaos theory and its application.
脑电(EEG)信号具有高维的混沌特性，混沌信号的预测是当前混沌理论与应用研究的一个重要方向。
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.
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。
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.
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。
Based on the EEG signal-processing system applying bispectral analysis, the results and their analysis obtained by processing EEG signals are given.
在研制脑电信号双谱分析处理系统软件及硬件的基础上，给出了对实际脑电信号处理所得到的实验结果及分析。
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.
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。
Processed by the MATLAB-based wavelet analysis, normal EEG signal is used as stimulating source to stimulate brain at proper points for insomnia treatment.
以正常的脑电作为刺激源，通过基于MATLAB的小波分析进行脑电数据处理，对特定穴位进行电刺激，达到治疗失眠的效果。
On the clinical medicine, the EEG signal processing offers not only the objective evidences for the diagnosis of some brain diseases but also effective therapeutical methods.
在临床医学方面，脑电信息处理不但为某些脑疾病的诊断提供了客观依据，而且为某些脑疾病提供了有效的治疗手段。
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.
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。
Here the focus is the front, designing a high classification BCI only using simple sensibilities mental task EEG signal and indication a method using this BCI to sort many kinds of tasks.
本文的工作重点在前端，设计了一种进行意识思维任务时脑电信号(EEG)的高辨识率二分类bci，并提出了一种应用此二分类bci进行多种工作任务识别的方法。
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.
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。
Main content:To set up real time anestheisa monitoring system with combination of the relationship of the Micro-dialysis's results and the EEG non-linear's results from primitive EEG signal.
主要内容：对原始脑电信息进行非线性分析，将微透析结果与脑电非线性分析结果相结合，建立脑电非线性实时监测分析系统。
So while false positives from the signal are possible, the combination of EEG and EMG data makes a false positive much less likely.
因此尽管可能出现假阳性信号，但是脑电图和肌电图的组合可以将假阳性的几率大幅降低。
Recent advances in sensors and signal processing, however, have helped close the gap, making the EEG-based approach more accurate and easier to learn how to use.
植入电极可以获得更加清晰的信号。然而，传感器和信号处理技术最近取得的进展，提高了采用EEG的第二种方法的准确性和便利性，可以有效弥补这个缺陷。
Electroencephalogram (EEG), as a principal signal in detecting brain activities, assumes a dominant position in the current research for the anesthetic depth monitoring.
脑电图作为检测大脑皮层活动的最主要信号，在目前麻醉深度监测研究中处于主导地位。
Correlation dimension is an important parameter to measure a nonlinear time sequence quantitatively, and it is widely used to analyze biomedical signal, such as EEG and ECG.
相关维数是定量描述非线性时间序列的一个重要参数，在脑电、心电等生物医学信号的特征描述方面得到了广泛地应用。
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.
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。
The second task of brain signal analysis is to extract EP signals from spontaneous EEG in strong backgrounds.
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。
EEG is a kind of complicated non-periodic bioelectricity signal and its research is still an important field for modern science.
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。
Recent advances in computer hardware and signal processing have made it feasible to use human electroencephalograph (EEG) signals to communicate with a computer.
近年来由于计算机硬件和信号处理技术的飞速发展，已经使得人们利用脑电信号与计算机之间进行通讯成为可能。
The contribution of this paper are:Firstly, we designed the hearing-evoked language task of EEG experiment. Weget lots of different songs as stimuli signal and collect some continuous EEG;
本文主要贡献在于：首先，设计了一组基于听觉诱发的语言任务相关的脑电实验，收集了大量不同种类的音乐作为刺激信号，并采集了一定数量的连续脑电信号；
The contribution of this paper are:Firstly, we designed the hearing-evoked language task of EEG experiment. Weget lots of different songs as stimuli signal and collect some continuous EEG;
本文主要贡献在于：首先，设计了一组基于听觉诱发的语言任务相关的脑电实验，收集了大量不同种类的音乐作为刺激信号，并采集了一定数量的连续脑电信号；
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