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.     

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.     

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     

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。     

基于扩展Infomax 独立分量分析算法的脑电信号消噪

背景：脑电信号能够反映大脑不同的生理病理状态,但在采集和分析处理过程中极易受到各种噪声的干扰,如眼球运动、眨眼、心电、肌电等,这些噪声的存在严重影响了脑电信号的分析和处理。 目的：介绍了一种基于扩展Infomax的独立分量分析方法,并用于脑电信号消噪。 方法：通过扩展Infomax算法的迭代求得分离矩阵,采用去除噪声分量后的独立成分重构需要记录的脑电信号,观察Matlab仿真得到的去噪后的脑电信号,同时比较去噪前后各导联脑电信号与眼电信号的相关性。 结果与结论：使用扩展Infomax 独立分量分析算法能够成功地去除多导脑电信号中的眼电干扰。再比较去噪前后各导联脑电信号的功率谱,可以发现使用扩展Infomax独立分量分析算法同时也能够成功地去除多导脑电信号中的工频干扰,且对脑电信号中的其他有用信号几乎没有破坏。     

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。     

基于经验模式分解和独立分量分析的单导少次EP信号提取

EP信号的单导少次提取一直是生物医学信号处理领域倍受关注的问题。本研究利用经验模式分解（EMD）,把单导脑电信号（EP＋EEG）分解成多个基本模式分量（IMF）之和,进而选取合适的基本模式分量或者它们的组合,构成1导或多导参考信号,再利用独立分量分析（ICA）成功提取出了期望的EP信号,从而克服了ICA需要多导观测信号的要求。仿真实验证明了本方法的有效性。     

运动意识脑电的动态独立分量分析

研究了用独立分量分析方法进行运动意识脑电信号特征分析的可行性。提出了用峭度极大动态独立分量分析方法进行μ节律提取的新思想。通过对批处理ICA算法和动态ICA算法在运动意识脑电特征分析的结果比较,得出了动态ICA算法更适合于运动意识脑电特征分析和提取。研究中发现,动态ICA混合矩阵系数的时间波形能准确即时地反映受试者进行左右手运动想象时运动神经皮层的μ节律变化,这一结果对脑认知和脑—机接口研究具有较大的实际意义,为独立分量分析方法在事件相关电位(ERP)特征提取中的应用提供了新的思路。     

基于结合小波变换与FastICA算法的脑电信号降噪(英文)

该文提出一种结合小波变换(WPT)与快速独立分量分析(Fast ICA)算法的方法来分析脑电信号。首先,原始脑电信号是通过使用WPT分解为三个层。然后,设置第三层最高频率的系数为零,以减少脑电信号的随机噪声,同时尽可能的保留信号的细节。其次,采取快速独立分量分析算法的优势,从脑电信号中分离所有类型的噪声。提出一种准预期值(QEV)的方法确定脑电图信号来自何处。最后,为了检验系统的性能,所有信道的相关信号在快速独立分量分析的输出进行分析。实验结果证实,交叉相关系数是10-15或10-16的量级,几乎可以被视为零。所提出性能良好的方法可以去除脑电信号所有类型的噪声。     

基于独立分量分析的脑电噪声消除

作为一种新的多元统计处理方法，独立分量分析(ICA)是解决盲源分离(BSS)问题的一个有效手段。在简要分析ICA理论及其算法的基础上，提出将其应用到脑电中的眼电伪迹的去除任务。实际采集的生理信号大多由相互独立的成分线性迭加而成，符合ICA要求源信号统计独立的基本假设。与传统方法相比，ICA这种空间滤波器不受信号频谱混迭的限制，消噪的同时能对有用信号的细节成分做到很好的保留，很大程度上弥补了时频域方法的不足。此外解混矩阵的逆可以用来反映独立源的空间分布模式，具有重要的生理意义。     

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.     

Removal of ocular artifacts from electro-encephalogram by adaptive filtering

The electro-encephalogram (EEG) is useful for clinical diagnosts and in biomedical research. EEG signals, however, especially those recorded from frontal channels, often contain strong electro-oculogram (EOG) artifacts produced by eye movements. Existing regression-based methods for removing EOG artifacts require various procedures for preprocessing and calibration that are inconvenient and timeconsuming. The paper describes a method for removing ocular artifacts based on adaptive filtering. The method uses separately recorded vertical EOG and horizontal EOG signals as two reference inputs. Each reference input is first processed by a finite impulse response filter of length M (M=3 in this application) and then subtracted from the original EEG. The method is implemented by a recursive leastsquares algorithm that includes a forgetting factor (λ=0.9999 in this application) to track the non-stationary portion of the EOG signals. Results from experimental data demonstrate that the method is easy to implement and stable, converges fast and is suitable for on-line removal of EOG artifacts. The first three coefficients (up to M=3) were significantly larger than any remaining coefficients.     

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.     

Detection of artifacts from high energy bursts in neonatal EEG

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.     

一种用ICA去除瞬态诱发耳声发射伪迹的新方法

如何去除伪迹是瞬态诱发耳声发射检测中一个关键的问题。本研究提出了一种用ICA去除伪迹的新方法。首先用四组线性增长的刺激声在耳道内录音 ,得到的波形是瞬态诱发耳声发射和伪迹的混叠。因为伪迹和瞬态诱发耳声发射是统计独立的 ,而且伪迹随刺激声的变化线性增长 ,而瞬态诱发耳声发射随刺激声的变化非线性增长 ,逐渐趋于饱和 ,所以它们在混叠信号中具有不同的混叠系数。用ICA算法可以将各独立分量及混叠矩阵估计出来 ,伪迹是其中的一个独立分量。然后将伪迹的波形置零后再进行一次混叠 ,便达到了去除伪迹的目的。最后通过与传统的DNLR方法比较 ,证明这种方法是有效的     

一种基于稀疏分解去除EEG信号中MRI伪迹的新方法

在脑电图(Electroencephalography,EEG)和功能磁共振成像(Functional magnetic resonance imaging, FMRI)同时记录时,如何有效的去除混入EEG信号中的强磁共振(Magnetic resonance imaging,MRI)伪迹干扰信号是当前在EEG和FMRI的联合研究中面临的一个信号前期处理难点。主要从MRI干扰信号和EEG信号在时空上的差别出发,提出了一种基于混合过完备库的稀疏成分分析的分解方法,实现了强MRI干扰下的EEG信号的估计。在方法实现中,首先利用小波和离散余弦构造能体现MRI干扰和EEG时空特性差别的混合过完备库,然后通过匹配追踪(Matching pursuit,MP)方法在混合过完备库中的学习,实现MRI伪迹的消除。对模拟数据以及真实记录的混入了MRI干扰的EEG信号的估计实验结果,证实了该方法的有效性。     

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.