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。     

脑电信号伪迹去除的研究进展

脑电(EEG)是一种反映大脑活动的生物电信号,由于它具有很高的时变敏感性,在采集时极易受到外界的干扰.如眼球运动、眨眼、心电、肌电等都会给真实的脑电信号加入噪声(伪迹).这些噪声给脑电信号的分析处理带来了很大的困难.从剔除EEG中的各种伪迹到去除噪声的效果评估研究者们都提出了很多方法.本文回顾了近些年提出的去除各种脑电信号伪迹的方法,包括回归方法、伪迹减法、主成分分析、独立变量分析和小波变换等,同时总结了各种方法的应用前提及各自的优点和不足,并对脑电信号的伪迹去除方法进行了展望.＃     

改进阈值的小波空域相关去噪法在荧光信号处理中的应用

为了去除荧光免疫层析检测中荧光信号的噪声,保留信号的细节信息,提出一种改进阈值的小波空域相关去噪算法。该算法将基于小波变换的空域相关去噪法和软阈值去噪法相结合,根据小波系数相关性的不同和平滑消去阈值法的思想,改进了软阈值去噪法的阈值变量和阈值函数。结果表明,该方法突出了信号边缘,能够有效地去除荧光信号的噪声,去噪后的信号光滑连续,且保留了信号峰的相关细节信息。     

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.     

基于独立成分分析的非侵入式脑-机接口眼电伪迹自动去除算法EI北大核心CSCD

非侵入式脑-机接口已经逐步成为当前研究的热点,在精神障碍检测、生理监测等多方面都有所应用。但是非侵入式脑-机接口所需的脑电信号容易受到眼电伪迹污染,会严重影响对脑电信号的解码分析。对此,本文提出了一种结合频率滤波器的改进型独立成分分析算法,以相关系数和峰度双重阈值为依据自动识别伪迹组件;利用眼电与脑电频率的差异,通过频率滤波器去除伪迹组件中的眼电信息,从而保留更多脑电信息。在公开数据集和本实验室数据上的实验结果表明,本文算法可以有效提升眼电伪迹去除效果,同时改善脑电信息损失,这有助于非侵入式脑-机接口的推广。     

基于改进小波阈值算法的ECG肌电信号去噪研究

为了解决传统软、硬阈值算法对肌电信号去噪后心电图（ECG）信号幅值降低和存在局部异常尖峰,导致去噪效果较差的问题。通过研究小波阈值算法的去噪原理和优化规则,基于双曲正切函数构造出一种具有连续性、结构简单、灵活性较高的可调阈值函数和改进的分层阈值,并分析得到小波分解含噪ECG信号的最佳小波基函数和分解层数,提出了一种改进的小波阈值算法。将软、硬阈值算法、相关文献中的阈值算法和本文所提改进阈值算法对含有真实肌电信号噪声的ECG信号进行去噪对比研究。实验结果表明：本文改进阈值算法能较好地去除ECG信号中的肌电信号噪声,并能更好地保持ECG信号波形特征,且Pearson相关系数值大于其他阈值算法。定性和定量结果表明,本文所提改进阈值算法对ECG肌电信号噪声具有较好的去噪效果。     

心电信号小波去噪的改进算法研究

对心电信号(ECG)中的基线漂移、工频干扰和肌电干扰等噪声进行去除,在波形识别、医疗诊断和治疗等领域具有重要意义。提出用sym5小波函数对心电信号进行8层小波分解。根据有用信号强度在每一层平均分配而噪声强度随分解层数增加而减少的规律,将分解得到的每一层的小波细节系数设置不同的阈值,最后用所提出的新阈值函数进行小波阈值去噪。该阈值函数既能克服硬阈值函数在阈值附近不连续的缺点,又可弥补软阈值函数与原函数之间存在固定差值的不足。以MIT-BIH心电数据库中的101号文件作为原始数据,将整个数据文件进行平均分段,每段有1 200个数据点,对每段数据进行加噪仿真分析,结果表明所提出的去噪算法得到的去噪信号信噪比比硬阈值函数和软阈值函数分别提高2.31%和8.04%,从而证明所提算法的有效性。     

一种基于模糊均差和小波变换的医学图像去噪方法

小波阈值萎缩法能够有效地去除图像中的噪声,去噪阈值直接影响去噪的效果,而噪声标准差在去噪阈值的确定中起着至关重要的作用。针对医学图像的特点、基于寻找更合适的噪声标准差估计方法,本研究提出了一种新的利用模糊均差代替普通标准方差估计噪声标准差的方法。在各层小波分解的低频图像中利用模糊积分估计噪声标准差,然后确定每一层去噪阈值,进行图像去噪。试验结果表明,本研究算法在去除噪声的同时也较好地保持了图像的细节。     

基于集合经验分解与改进阈值函数的小波变换心电信号去噪方法研究

针对心电图(ECG)信号去噪问题,提出了一种基于集合经验分解(EEMD)和改进阈值函数的小波变换去噪方法。首先利用EEMD对含噪的ECG信号进行分解,选取固有模态函数(IMF),重构ECG信号,实现ECG信号的一次去噪;再利用改进阈值函数的小波变换方法对ECG信号进一步去噪。实验中,利用MIT-BIH心电图数据库对提出的方法进行评估,用参数信噪比(SNR)和均方误差(MSE)比较EEMD、改进阈值函数的小波变换方法以及本文提出的方法的去噪效果。实验结果表明:本文提出的方法去噪后的ECG信号波形平滑,特征点幅值无衰减,在去噪的同时更好地保留了原始ECG信号的特征。     

微波热疗凝固区域超声回波信号的小波去噪方法

目的 针对用于监测微波热疗凝固区域的超声回波信号信噪比较低,强反射点较多,难以定位凝固区域边缘的特点,研究了一种基于小波分解的去噪方法.方法 在理论分析的基础上,对超声回波信号进行小波分解,根据不同频段信号的特征,进行局部分层小波阈值去噪,再通过小波重构得到去噪后的超声回波信号.结果 对比硬阈值去噪、软阈值去噪和本文所采用方法的效果,探讨了利用本文算法进行凝固区域边缘识别的可行性.结论 局部分层小波去噪算法可有效抑制噪声,保留信号的细节特征,达到优化超声回波信号的目的.     

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.     

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.     

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%.     

脑电棘波识别和噪声消除的小波变换方法

研究了利用二进小波变的的模极大值识别脑电信号奇异点如棘波和消除噪声的方法，该方法在较好保留原脑电信号奇异信息的同时能有效地消除噪声，进一步讨论了信号与白噪声的奇异性指数的区别，以及小波变换模极大值沿各变换尺度传递的不同特性，并利用该特性区分信号中的奇异点和噪声，能准确识别奇异点的位置，这种奇异性识别技术在信号的特征提取和消除噪声方面有广阔的应用前景。     

An automatic analysis method for detecting and eliminating ECG artifacts in EEG

An automated method for detecting and eliminating electrocardiograph (ECG) artifacts from electroencephalography (EEG) without an additional synchronous ECG channel is proposed in this paper. Considering the properties of wavelet filters and the relationship between wavelet basis and characteristics of ECG artifacts, the concepts for selecting a suitable wavelet basis and scales used in the process are developed. The analysis via the selected basis is without suffering time shift for decomposition and detection/elimination procedures after wavelet transformation. The detection rates, above 97.5% for MIT/BIH and NTUH recordings, show a pretty good performance in ECG artifact detection and elimination.     

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.     

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.     

Differential Contributions of Blinks and Vertical Eye Movements as Artifacts in EEG Recording

Blinks and vertical eye movements were studied as artifacts of EEG recording. The electro-oculogram (EOG) and vertex vs joined mastoids EEG were recorded in 13 college-aged subjects. Subjects were asked to blink “normally, without excessive effort,” and move their eyes through vertical visual arcs of 5°, 10°, 20°, 30°, and 60°. The ratio EEG/EOG, the fraction of the EOG potential transmitting to the scalp EEG electrode as artifact, was calculated for potentials generated during both blinks and eye movement. Vertical eye movement scalp EEG artifact was a constant percentage of the vertical eye movement EOG across visual arcs of 10° to 60°. Mean percentage eye blink EEG artifact (9.3%) was significantly (p < .001) less than the mean percentage vertical eye movement artifact (13.9%). Thus, blink and vertical eye movement artifact fields are quantitatively different in terms of their transmission to the scalp (C_z) EEG electrode. Subtraction of a single subject specific percentage of the EOG from the EEG would correct for either artifact source, but different subtraction percentages must be used for each.     

癫痫脑电棘波的小波变换模极大值对检测方法

本文首次将对小波变换模极大值对检测信号奇异点的理论应用于癫痫脑电信号,对棘波进行检测。采用二进样条小波脑电信号按Ｍａｌｌａｔ算法进行变换,分析含有奇异点的信号,即棘波,与其小波变换模数大值对的关系,对棘波进行识别。