基于独立分量分析的生理信号盲源分离

用于盲源分离的独立分量分析（ICA）和扩展ICA算法，基于极大似然估计，给出一个衡量输出分量统计独立的目标函数，最优化目标函数，得到一种用于独立分量分析的迭代算法。扩展ICA算法的优点在于迭代过程中不需要计算信号的高阶统计量，收敛速度快，同时适用于超高斯和亚高斯信号的分离。应用该算法实现了脑电、心电信号以及语音信号的分离，并给了实验结果。     

约束独立分量分析及其在脑电信号伪差分离中的应用

独立分量分析(ICA)算法是一种运用统计方法,从一系列标准信号中提取独立成分的技术.由于脑电信号是由若干相对独立的成分组成,所以运用ICA算法来处理脑电信号受到广泛关注.本文介绍了一种新型的约束独立分量分析(cICA)算法,它能解决FastICA算法在源信号分离时输出排列无序性的问题.并通过实验表明,它在脑电伪差分离时可减少人工处理的影响,且具有良好的稳健性与较快的收敛速度.     

空间ICA在fMRI数据上的应用与分析

独立成分分析(ICA)技术试图将多维数据分解成若干个相互统计独立的分量。时间ICA和空间ICA都可以用于分析功能核磁共振成像(fMRI)数据。但由于fMRI数据空间维数远远大于时间维数，为计算方便，在分析fMRI数据时。则更多的使用空间ICA方法。本文在单任务激励实验中，利用ICA方法从fMRI数据中分离出若干个与任务相关的独立分量，其中包括与任务相关的恒定分量(CTR)和与任务相关的暂态分量(TTR)；通过将这些独立分量进行空间映射，得到了与任务相关的脑部激活区域。将此结果与SPM的分析比较，得到了一致的结果。在对结果的分析中，我们进一步指出了ICA方法的特点和局限性。     

基于独立分量分析的生理信号盲源分离

用于盲源分离的独立分量分析 (ICA)和扩展ICA算法 ,基于极大似然估计 ,给出一个衡量输出分量统计独立的目标函数 ,最优化该目标函数 ,得到一种用于独立分量分析的迭代算法。扩展ICA算法的优点在于迭代过程中不需要计算信号的高阶统计量 ,收敛速度快 ,同时适用于超高斯和亚高斯信号的分离。应用该算法实现了脑电、心电信号以及语音信号的分离 ,并给出了实验结果     

ICA在心音信号处理中的应用

独立成分分析(ICA)是近年来涌现的用于盲信号分离的新技术.本研究利用独立成分分析成功地把心音信号分离为三个独立的成分.借助ICA方法我们能够有效地区分正常人和房间隔缺损病人的心音信号.研究中所用的心音信号由自行研发的心音采集仪所采集.文章首先介绍了独立成分分析的基本原理,然后介绍了一种基于四阶统计量的算法-快速定点算法,并给出了利用此算法的ICA步骤.试验结果表明,ICA在心音信号的处理中是一种很有潜力的方法.     

独立成分分析及其应用的研究进展

独立成分分析(ICA)是一项把混合信号分解成具有统计独立性成分的新技术。ICA近年已在生物医学和雷达等领域的信号分离中展示了很好的应用前景。我们比较系统地介绍了ICA的基本原理、主要算法、应用和将来ICA研究的发展方向，旨在进一步推动有关的理论与应用研究工作。     

快速独立分量分析及其在脑电信号处理中的应用

独立分量分析(Independent Component Analysis, ICA)是一种基于信号统计特性的盲源分离方法,由于其分离的信号之间是互相独立的,所以在生物电信号去除干扰和伪迹、信号分离以及特征提取等方面有很大的潜在价值.本文提出了一种改进的快速ICA方法,提高了收敛速度.通过仿真,证明这种方法的优越性.最后利用该方法去除脑电中眼动伪迹,达到了较好的效果.     

独立分量分析及其在生物医学工程中的应用

:独立分量分析 ( Independent Component Analysis,简记 ICA)是信号分解技术的新发展。ICA与 PCA(主分量分析 )或 SVD(奇异值分解 )的主要不同是 :后者分解得的各分量只是互不相关 ,而前者则要求各分量相互统计独立。体表测量得的信号往往包含若干相对独立的成分 ,因此采用ICA技术来分解 ,所得结果往往更有生理意义 ,有利于去除干扰和伪迹。本文简短地回顾 ICA的基本原理、判据、算法和其在生物医学工程中的应用 ,并作出展望及指出存在问题。     

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.     

基于小波变换和独立分量分析相结合的癫痫脑电分析

临床上分析癫痫脑电信号非常重要。由于临床记录的癫痫脑电信号中含有大量的伪迹干扰,特别是肌电伪迹,所采集的脑电信号无法正确反映大脑的生理及病理状况。本研究利用小波变换的多分辨率特性和独立分量分析(ICA)的盲源分离特性,把用连续小波变换分解的脑电子带信号作为ICA输入,经ICA分离后,有效地消除了癫痫脑电中的肌电伪迹,并分离出了癫痫样特征波,效果理想。     

Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis

Muscle artifacts are typically associated with sleep arousals and awakenings in normal and pathological sleep, contaminating EEG recordings and distorting quantitative EEG results. Most EEG correction techniques focus on ocular artifacts but little research has been done on removing muscle activity from sleep EEG recordings. The present study was aimed at assessing the performance of four independent component analysis (ICA) algorithms (AMUSE, SOBI, Infomax, and JADE) to separate myogenic activity from EEG during sleep, in order to determine the optimal method. AMUSE, Infomax, and SOBI performed significantly better than JADE at eliminating muscle artifacts over temporal regions, but AMUSE was independent of the signal-to-noise ratio over non-temporal regions and markedly faster than the remaining algorithms. AMUSE was further successful at separating muscle artifacts from spontaneous EEG arousals when applied on a real case during different sleep stages. The low computational cost of AMUSE, and its excellent performance with EEG arousals from different sleep stages supports this ICA algorithm as a valid choice to minimize the influence of muscle artifacts on human sleep EEG recordings.     

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.     

基于带参考信号的ICA算法的脑电信号眨眼伪差的分离研究

独立分量分析（ICA）是一种从混合信号中提取统计独立的分量的一种方法.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.这个方法的主要思路是：先选取一导眨眼伪差比较明显的数据,从中获得眨眼伪差的参考信号,再用ICA方法把眨眼伪差第一个提取出来,最后得到消除伪差后的EEG信号.详细讨论了使用带参考信号的ICA算法消除眨眼伪差的方法与步骤,并给出了应用于真实信号的实验结果.     

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.     

QRS artifact elimination on full night sleep EEG

Spectral analysis is now a standard procedure for analyzing the electroencephalograms (EEG) obtained by polysomnographic recordings. These numerical methods assume an artifact-free EEG since artifacts create spurious spectral components. Our aim was the development of a QRS artifact removal technique that might be applied to full night EEG with a minimal human intervention. This technique should handle one EEG channel, with or without use of one ECG channel. Variance minimization, independent component analysis (ICA), morphological filters (MF) have been implemented. Careful attention has been given to define the MF structuring element. The tests on artifact-simulated and real data were checked on the residual ECG spectral components present in the cleaned EEG. The best results are obtained by the MF when the structuring element is an artifact template defined either directly on the EEG or on the ICA ECG component. Further developments are required to identify and subtract the T-wave artifacts.     

A fully automatic ocular artifact suppression from EEG data using higher order statistics: Improved performance by wavelet analysis

Contamination of electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent component analysis (ICA) is now a widely accepted tool for detection of artifacts in EEG data. One major challenge to artifact removal using ICA is the identification of the artifactual components. Although several strategies were proposed for automatically detecting the artifactual component during past several years, there is still little consensus on the criteria for automatic rejection of undesired components. In this paper we present a new identification procedure based on an efficient combination of independent component analysis (ICA), mutual information, and wavelet analysis for fully automatic ocular artifact suppression. The method does not require any offline training or determining the threshold levels for different markers. The results show that the proposed method could significantly enhance the ocular artifact detection and suppression. The results on 3105 4-s EEG epochs indicate that the artifact components can be identified with an accuracy of 97.8%, a sensitivity of 96.9%, and a specificity of 98.6%.     

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.     

Correction of blink artifacts using independent component analysis and empirical mode decomposition

Blink‐related ocular activity is a major source of artifacts in electroencephalogram (EEG) data. Independent component analysis (ICA) is a well‐known technique for the correction of such ocular artifacts, but one of the limitations of ICA is that the ICs selected for removal contain not only ocular activity but also some EEG activity. Straightforward removal of these ICs might, therefore, lead to a loss of EEG data. In this article a method is proposed to separate blink‐related ocular activity from actual EEG by combining ICA with a novel technique, empirical mode decomposition. This combination of two techniques allows for maximizing the retention of EEG data and the selective removal of the eyeblink artifact. The performance of the proposed method is demonstrated with simulated and real data.     

Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis

Temporal independent component analysis (ICA) is applied to an electrophysiological signal mixture (such as an EEG recording) to disentangle the independent neural source signals—independent components—underlying said signal mixture. When applied to scalp EEG, ICA is most commonly used either as a pre-processing step (e.g., to isolate physiological processes from non-physiological artifacts), or as a data-reduction step (i.e., to focus on one specific neural process with increased signal-to-noise ratio). However, ICA can be used in an even more powerful way that fundamentally expands the inferential utility of scalp EEG. The core assumption of EEG-ICA—namely, that individual independent components represent separable neural processes—can be leveraged to derive the following inferential logic: If a specific independent component shows activity related to multiple psychological processes within the same dataset (e.g., elicited by different experimental events), it follows that those psychological processes involve a common, non-separable neural mechanism. As such, this logic allows testing a class of hypotheses that is beyond the reach of regular EEG analyses techniques, thereby crucially increasing the inferential utility of the EEG. In the current article, this logic will be referred to as the ‘common independent process identification’ (CIPI) approach. This article aims to provide a tutorial into the application of this powerful approach, targeted at researchers that have a basic understanding of standard EEG analysis. Furthermore, the article aims to exemplify the usage of CIPI by outlining recent studies that successfully applied this approach to test neural theories of mental functions.