独立分量分析的研究和脑电中心电干扰的消除

本文研究和提出了一种用于独立分量分析的迭代算法 ,采用该算法成功地消除了存在于脑电信号中的心电干扰。基于信息论原理 ,给出了一个衡量各分量统计独立的目标函数 ,优化该目标函数 ,得出一种用于对独立分量进行盲分离的迭代算法 ,该算法的优点在于不需要计算信号的高阶统计量 ,收敛速度快。该算法使用一种去冗余方法 ,在提取一分量后 ,将其从混迭信号中去除 ,能逐一提取各独立分量。实验结果表明独立分量分析可有效地去除脑电信号中的心电干扰成分     

基于变分模态分解的眼电伪迹去除

脑电信号可以反映人体大脑活动状态,精确地将脑内信息传递向外界,对脑科学研究具有重要的意义。在实际情况中,脑电信号采集的同时会带有一些噪声,而眼电伪迹的存在会严重干扰脑电信号。本研究尝试了一种基于变分模态分解的眼电伪迹去除方法。通过变分模态分解将采集到的脑电信号分解成K组模态分量;根据眼电伪迹的频率特点,选择出眼电伪迹所对应的模态分量,并将其去除后重新构建剩余的模态分量。结果表明通过对实验数据的处理,变分模态分解可以有效地将眼电伪迹去除,并维持脑电信号的特征。     

基于脑电信号的麻醉特征参数分析

针对所有原始脑电信号都受到低频和尖峰噪声干扰的问题,提出了小波变换和独立分量分析相结合的去噪算法;对预处理后的脑电数据,进行小波熵、近似熵和复杂度这三种特征参数的数值表征,并进一步通过特征参数的状态变化率来判断脑电信号的状态区分效果。麻醉与非麻醉的脑电数据处理结果表明,三种特征参数的状态变化率分别达到50.5%、21.6%和19.5%,其中小波熵的状态变化率最高,这些特征参数可作为基于脑电信号分析的麻醉深度量化研究的基础。     

基于遗传算法的多通道癫痫脑电信号盲源分离

目的：研究一种将心电噪声信号从脑电信号中分离出来的算法及其DSP硬件实现。方法：癫痫是一种中枢神经系统疾病，该病的诊断主要依靠脑电监测，但由于人体是一个复杂网络，临床采集到的脑电通常会混有其他噪声如心电干扰，这为后续的处理引入不可控制的误差。本文采用基于遗传算法的独立分量方法实现多通道脑电信号的盲源分离。结果：通过相关临床专家检验，认为该方法基本能够去除心电噪声，和参考心电信号对比具有一致性。结论：通过从北京某三甲医院癫痫中心采集到的患者脑电数据进行测试，对比试验表明，该方法是一种稳健高效的处理方法，符合并行运算的特点，整套算法可以移植到基于DSP的嵌入式系统架构上，具有一定的实用价值。     

基于典型相关分析和小波变换的眼龟伪迹去除

目的针对脑电信号中眼电伪迹去除尚存在的问题，提出一种基于典型相关分析与小波变换的（wavelet—enhanced canonical correlation analysis，wCCA）自动去除眼电伪迹的算法。方法首先，充分利用脑电信号和眼电伪迹的空间分布特征，将基于典型相关分析的盲源分离算法分别应用于左右脑区的混合信号中，从而保证典型相关分析分解得到的第一个典型相关变量（即左右脑区之间的最公共成分），就是眼电伪迹分量。然后为了恢复泄漏在该伪迹分量中的脑电成分，对伪迹分量进行小波阈值滤波，将高于某一阈值的小波系数置零，而保留低于阈值的系数。结果与其他三种基于盲源分离去除眼电伪迹的方法相比较，该方法在有效地自动去除眼电伪迹的同时，很好地保留了潜在的脑电信号，去除效果明显优于其他三种方法。结论由于该算法简单，处理速度较快，因此应用于实时的脑机接口系统中更具优越性，为后续脑电信号的特征提取和分类分析提供了良好的基础。     

基于固有模态分解和深度学习的抑郁症脑电信号分类分析

以采集到的抑郁症患者和正常人的脑电信号为基础,采用固有模态分解算法对原始信号去噪处理,通过卷积神经网络对抑郁症患者和正常人进行分类分析。首先通过脑电信号的采集实验,采集15位抑郁症患者和15位正常人对照组Fp1的静息态脑电信号;之后对采集到的静息态脑电进行去噪处理,脑电去噪处理主要包括固有模态分解算法对原始信号的分解获得不同层次的IMF分量,对IMF分量进行频域分析,通过硬阈值的方法剔除原始信号中的噪声信号;最后采用卷积神经网络对抑郁症患者和正常人对照组进行二值分类,结果相较于传统的特征提取-机器学习算法,分类准确率明显提高。     

基于小波奇异点检测和阈值去噪的眨眼伪迹去除方法

目的眨眼伪迹是脑电中一种常见且影响严重的伪迹。本论文提出一种基于小波奇异点检测和阈值去噪的眨眼伪迹去除方法,无需眼电参考信号,做到自动去除单导脑电信号中的眨眼伪迹。方法首先利用小波奇异点检测特性以检测眨眼伪迹的峰值位置,然后只对眨眼伪迹区域进行小波阈值去噪。结果实验结果表明,本方法能够有效检测眨眼伪迹,避免了普通方法去噪时对非眨眼区域的影响。结论本方法使用的阈值和阈值函数优于典型的阈值和软、硬阈值函数,有效地去除了脑电中的眨眼伪迹。     

一种基于皮层成像的自动眼电伪迹去除方法

脑电信号十分微弱,并且特别容易受到眼电的干扰.这些干扰给阅读和分析脑电信号带来了很大的困难,因此自动消除眼电对脑电的干扰一直是研究人员重视的问题.本研究提出一种基于皮层成像的自动眼电伪迹去除方法,对于已经完成滤波的脑电数据段,通过设立阈值的方法识别伪迹,利用基于相关系数的眼电伪迹识别算法标记眼电伪迹数据段,然后通过结合脑电信号时空信息的、基于皮层成像技术的眼电伪迹处理方法(CAST),处理已经标记好的眼电伪迹数据段,并通过真实的事件相关电位数据验证了方法的有效性.验证结果表明,此方法能够实现眼电伪迹的自动识别和去除,去除伪迹后的信号与原始无眼电伪迹的标准信号之间的相关系数为0.953 7±0.042 3.     

独立分量分析在脑电信号处理中的应用及研究进展

独立分量分析(independent component analysis,ICA)方法是从一组观测信号中提取统计独立分量的方法.因为用这种方法分解出的各信号分量之间是相互独立的,而测得的脑电信号往往包含若干相对独立的成分,所以用它来分解脑电信号,所得的结果更具有生理意义,有利于去除干扰和伪差.本文简要地回顾了ICA的发展历史和主要算法,综述了它在脑电信号处理中的应用及研究进展,并指出了需要进一步研究解决的问题.     

一种基于脑电信号的疲劳驾驶状态判断方法

通过研究疲劳驾驶时脑电信号的特征,提出了一种基于独立分量分析（independent component analysis,ICA）的脑波疲劳状态判断方法.利用模拟驾驶系统,采用NT-9200动态脑电仪采集驾驶员在清醒和疲劳状态下（连续驾驶4h以上）的脑电信号,对采集的多导信号进行独立分量分析,去除EEG信号中的眼电、肌电及工频等干扰,经过快速傅里叶变换（fast fourier transform,FFT）后计算出脑波中多种功率谱密度,求得疲劳指数F.实验结果表明,在疲劳状态下的疲劳指数F明显高于清醒状态下的F.本文提出的脑波疲劳状态判断方法可有效用以判断驾驶员的疲劳程度.     

基于独立分量分析的诱发电位信号噪声分离方法

诱发电位(EP)信号的检测与分析技术是临床医学诊断神经系统损伤及病变的重要手段之一。但是，从人体体表所得到的EP信号含有大量的噪声，最典型的噪声是人体自发产生的脑电图信号(EEG)。因此，为利用EP信号诊断神经系统的损伤和病变，需要从混合信号中去除EEG等噪声。独立分量分析(ICA)是一种新近发展起来的统计信号处理方法。本文把ICA方法应用于EP信号的噪声消除，并与传统的自适应滤波方法进行了比较。计算机模拟表明，采用ICA方法进行信号噪声分离的结果明显优于自适应滤波方法。     

Removing electroencephalographic artifacts by blind source separation

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.     

Is ocular voltage propagation to the electroencephalogram frequency dependent?

Conventional eye correction methods subtract portions (propagation coefficients; Bs ) of electrooculogram (EOG) voltages from the electroencephalogram (EEG). The frequency domain approach (FDA) uses different B s for different frequencies whereas the time domain approach (TDA) uses the same B s. To determine whether measured B s are dependent on frequency and whether one should employ frequency-dependent methods, 20 min of EEG from eye movement (EM) and blink data (24 participants) were recorded, and B s were calculated for eye movement ERPs of differing signal-to-noise ratios for frequency bands ranging from 0 to 40 Hz and compared. At high signal to noise, EM B s for different frequency bands did not differ, for both vertical and horizontal EOG, at all scalp sites tested. There were small differences in blink B s for different bands, but smaller than the margin of error of this analysis. This indicates that TDA may be more appropriate than FDA.     

Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP

Eye movement artifacts in electroencephalogram (EEG) recordings can greatly distort grand mean event‐related potential (ERP) waveforms. Different techniques have been suggested to remove these artifacts prior to ERP analysis. Independent component analysis (ICA) is suggested as an alternative method to “filter” eye movement artifacts out of the EEG, preserving the brain activity of interest and preserving all trials. However, the identification of artifact components is not always straightforward. Here, we compared eye movement artifact removal by ICA compiled on 10 s of EEG, on eye movement epochs, or on the complete EEG recording to the removal of eye movement artifacts by rejecting trials or by the Gratton and Coles method. ICA performed as well as the Gratton and Coles method. By selecting only eye movement epochs for ICA compilation, we were able to facilitate the identification of components representing eye movement artifacts.     

The Removal of Ocular Artifacts from EEG Signals Using Adaptive Filters Based on Ocular Source Components

Ocular artifacts are the most important form of interference in electroencephalogram (EEG) signals. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the patient. In contrast, blind source separation (BSS) is a method of decomposing multiple EEG channels into an equal number of source components (SCs) by independent component analysis. The ocular artifacts significantly contribute to some SCs but not others, so uncontaminated EEG signals can be obtained by discarding some or all of the affected SCs and re-mixing the remaining components. BSS can be performed without EOG data. This study presents a novel ocular-artifact removal method based on adaptive filtering using reference signals from the ocular SCs, which avoids the need for parallel EOG recordings. Based on the simulated EEG data derived from eight subjects, the new method achieved lower spectral errors and higher correlations between original uncorrupted samples and corrected samples than the adaptive filter using EOG signals and the standard BSS method, which demonstrated a better ocular-artifact reduction by the proposed method.     

A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal

Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG–fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.     

基于自适应局部余弦变换的正交多普勒超声信号降噪方法的研究

多普勒超声信号的谱图已经被广泛用于医疗诊断。来自系统内部的噪声及外部的干扰会产生附加的频谱成分，从而影响谱图的主观分析及进一步的定量分析。为抑制噪声的影响，本文提出利用一种新的基于自适应局部余弦变换和非负Garrote取阈值的方法对正交多普勒超声信号进行降噪。首先，由正交信号提取正向和逆向血流信息；然后对其分别进行降噪；最后利用Hilbert变换进行重构得到真实信号的估计。在仿真研究中，采用平均频率波形和谱宽波形的估计精度作为性能改善的指标。结果表明这种方法优于基于小波变换的降噪方法，特别是在低信噪比情况下。     

Tracking eye fixations with electroocular and electroencephalographic recordings

We describe a method, based on recordings of the electroencephalogram (EEG) and eye movement potentials (electrooculogram), to track where on a screen (x,y coordinates) an individual is fixating. The method makes use of an empirically derived beam-forming filter (derived from a sequence of calibrated eye movements) to isolate eye motion from other electrophysiological and ambient electrical signals. Electrophysiological researchers may find this method a simple and inexpensive means of tracking eye movements and a useful complement to scalp recordings in studies of cognitive phenomena. The resolution is comparable to that of many commercial systems; the method can be implemented with as few as four electrodes around the eyes to complement the EEG electrodes already in use. This method may also find some specialized applications such as studying eye movements during sleep and in human-machine interfaces that make use of gaze information.     

Automatic Removal of Eye-Movement and Blink Artifacts from EEG Signals

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.     

Doppler ultrasound wall removal based on the spatial correlation of wavelet coefficients

In medical Doppler ultrasound systems, a high-pass filter is commonly used to reject echoes from the vessel wall. However, this leads to the loss of the information from the low velocity blood flow. Here a spatially selective noise filtration algorithm cooperating with a threshold denoising based on wavelets coefficients is applied to estimate the wall clutter. Then the blood flow signal is extracted by subtracting the wall clutter from the mixed signal. Experiments on computer simulated signals with various clutter-to-blood power ratios indicate that this method achieves a lower mean relative error of spectrum than the high-pass filtering and other two previously published separation methods based on the recursive principle component analysis and the irregular sampling and iterative reconstruction, respectively. The method also performs well when applied to in vivo carotid signals. All results suggest that this approach can be implemented as a clutter rejection filter in Doppler ultrasound instruments.