结果：消除了脑电信号中的噪声和心电干扰。
该方法在脑电信号分析中取得了很好的成效。
This method effects excellently in the analysis of EEG signals.
前言：目的：去除脑电信号中的噪声和心电干扰。
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.
脑电信号是从头皮表面记录到的脑细胞群的自发性电活动。
Electroencephalogram (EEG) is the spontaneous electrical activity recording of brain cells from scalp.
脑电信号的获取对脑功能状态检测和脑部疾病的诊断具有重要意义。
The acquisition of EEG is very important for detecting the function state of brain and the diagnosis of some brain disease.
该方法通过计算排序模式分布的距离来分析两段脑电信号的相异性。
The dissimilarity between two EEG epochs can be qualified via a simple distance measure between the distributions of order patterns.
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.
利用该方法可以使系统具有零相位特性，实现脑电信号的零相位失真滤波。
By using these methods, the system can be of the feature of zero phase error, and the filtering of EEG signals with zero phase error can be realized.
目的：探讨盐酸纳洛酮治疗急性脑卒中致意识障碍的疗效及脑电信号的影响。
AIM: To explore the curative effect of naloxone in treating disturbance of consciousness caused by acute stroke and its influence on brain electrical signal.
设计一种仅使用进行简单思维任务时脑电信号(脑电图)的高准确率脑机接口。
A high accuracy BCI is designed using electroencephalogram EEG signals where the subjects have to think of only a single mental task.
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。
KICA with model selection step is applied to the task of removing ECG artifact from the EEG signal and the result shows KICA.
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.
针对特定思维诱发脑电信号的特点，提出一种确定其分布情况及提取其波形的方法。
Based on the characteristics of the specific thinking-evoked EEG signals, an approach is proposed to determine their distribution and pick up their waveform out of strong noises.
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。
Based on analyzing the character of EEG signal, we design the AEEG recorder with 18 channels.
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).
由于脑电信号可以通过无创检测得到，因此对脑电信号的研究就成为脑研究的热点之一。
Due to deriving the EEG data but having no hurt to human, research on electrical activity in brain is the focus.
最后，本文对脑电信号分类、癫痫脑电特征提取的发展前景以及应用趋势做了相关探讨。
Finally, this paper makes an evaluation on the prospect and application of EEG classification and epilepsy feature extraction.
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。
The 12-bit ADC in C8051F020 samples the EEG signal amplified by the preamplifier.
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.
脑电信号功率谱阵图计算机分析显示了一定的优越性，有望为诊断此类患儿提供客观指标。
The obvious advantages of using computer analysis may be the indexes to diagnosis this kind of disease.
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.
结论：脑电信号相位相干性指数随时间的变化与事件相关去同步和事件相关同步现象相一致。
CONCLUSION: The mean phase coherence of EEG changed with time is coincident with event-related desynchronization and event-related synchronization.
目前有很多经典的信号处理和分析方法可用于脑电信号的分析和分类，并取得了较好的效果。
At present, Numbers of signals processing and analyzing method are used in EEG research work and have good performance.
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.
脑电信号种包含大量的生理和病理信息，在睡眠相关疾患和脑科学研究中起着非常重要的作用。
EEG signals have a large amount of physiology and pathology information, electrical activity of the brain plays a very important role in the field of the related disease of sleep and brain science.
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.
脑电信号(EEG)是中枢神经系统产生的生物电活动，它包含了丰富的神经系统状态和变化的信息。
EEG are bioelectrical activity generated by the central nervous system, it contains a lot of the information about status and changes in the nervous system.
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