脑电信号预处理方法研究综述

脑电信号是一种复杂且重要的生物信号,被广泛应用于类脑智能技术和脑机接口领域的研究。文中介绍了干扰正常脑电信号的常见非生理性伪迹和生理性伪迹的类型及特点,并对生理性伪迹的产生原因进行了详细分析。通过对各种脑电信号去除伪迹方法的回顾以及应用现状的分析,比较并总结了传统去除伪迹方法和新型去除伪迹方法的研究进展,并进一步分析去除伪迹方法的优缺点。部分方法已经成功应用于处理脑电信号中的眼电、心电和肌电等伪迹中。文中还针对目前脑电信号去除伪迹的需求及所面临的问题给出了应对策略,并对未来的研究方向进行了分析和展望。     

眼电伪迹自动识别与去除的新方法

为了改善脑电中的眼电伪迹过估计问题及环境干扰耦合引起的非线性混合对眼电去除效果的影响,提出一种基于快速核独立成分分析(Fast Kernel Independent Component Analysis,FastKICA)与离散小波变换(Discrete Wavelet Transform,DWT)的眼电自动去除方法,即(Fast Kernel Independent Wavelet Transform ,FKIWT)方法.首先,利用FastKICA方法对脑电信号进行分离得到独立成分,并以相关系数为依据识别出眼电伪迹;进而,基于DWT对眼电伪迹进行多分辨率分析,将逼近分量置零,而细节分量保持不变,使得重构所得眼电伪迹成分保留更多有用脑电信号;最后,利用FastKICA逆变换重建眼电去除后的脑电信号.实验结果表明:FKIWT不仅有效改善了眼电过估计问题,增强了抗干扰能力和鲁棒性,而且在线性混合和非线性混合情况下,均得到较好的伪迹去除效果,特别是在非线性混合时优势更为明显,适合于实际在线应用.     

脑电信号中眼电伪迹自动去除方法的研究

 针对实际采集的脑电信号受眼电干扰较大,提出一种基于离散小波变换(DWT)与独立分量分析(ICA)的自动去除眼电伪迹的方法(DWICA).对采集的多导脑电和眼电信号进行离散小波变换,获取多尺度小波系数,将串接小波系数作为ICA的输入;利用基于负熵判据的FastICA算法实现独立成分的快速获取,引入夹角余弦准则自动识别眼迹成分,并经过ICA逆变换将剔除眼迹后的独立成分投影返回到原脑电信号各个电极;通过DWT逆变换重构信号,即可得到去除眼迹的各导脑电信号.实验结果表明,DWICA方法极大地提高了脑电信号的信噪比,抗噪能力强且实时性好,为脑电信号的在线预处理提供了新思路.     

基于最大信噪比盲源分离的脑电信号伪迹滤波算法

心电和眼电伪迹是脑电信号中最常见的干扰,本文提出一种基于最大信噪比盲源分离的伪迹滤波算法.该算法以分离矩阵为变元建立源信号的信噪比目标函数,寻找能使目标函数达到极大(或极小)值的分离矩阵,进而通过分离矩阵求得估计信号.算法的实施过程是,首先利用小波变换去除在原始脑电信号中的部分噪声,然后用基于最大信噪比盲源分离的伪迹滤...     

基于独立分量分析的多通道脑电信号自动去噪算法研究

在脑电信号的采集和处理过程中,经常受到如眼电、心电等各样噪声和伪迹的影响。独立分量分析通过对非高斯分布数据进行有效表示,获得在统计学上独立的各个分量,通过对噪声分量的去除以及信号分量的重构,实现对噪声和伪迹的去除。针对目前信号分解后噪声分量的处理尚停留在目测去除和人工识别阶段,耗时严重以及准确度差的不足,本文提出一种基于独立分量分析的KC复杂度自动阈值算法的提出很好地解决了这个问题,在对含工频噪声的EEG信号进行处理后,取得了良好的实验效果。     

基于独立向量分析的脑电信号中肌电伪迹的去除方法

脑电数据经常被各种电生理信号伪迹所污染。在常见伪迹中,肌电伪迹特别难以去除。文献中最常用的方法包括诸如独立分量分析(Independent Component Analysis, ICA)和典型相关分析(Canonical Correlation Analysis, CCA)等盲源分离技术。该文首次提出一种基于独立向量分析(Independent Vector Analysis, IVA)的新方法,用以去除脑电中的肌电伪迹。IVA同时使用高阶统计量和二阶统计量,因此该方法能够充分利用肌电伪迹的非高斯性和弱相关性,兼具ICA方法和CCA方法的优势。实验表明,使用IVA方法可以在保留脑电成份的同时极大抑制肌电伪迹,效果显著优于ICA法和CCA法。     

在线Infomax算法及其在长记录脑电消噪中的应用

基于信息极大原理的Infomax算法是一种非常有效的盲源分离算法,在信号处理领域中得到了广泛的应用.本文在传统Infomax算法的基础上,提出了一种在线Infomax算法.文中对在线Infomax算法的性能进行了分析和验证,并结合长时间记录的实测脑电信号中的心电伪迹消除问题,对在线Infomax算法和传统Infomax算法的性能进行了进一步的分析比较.实验结果证明,本文提出的在线Infomax算法在时变混合模型中具有良好的收敛性能,并能有效地消除脑电信号中的心电伪迹.     

虚拟现实视觉体验对事件相关电位影响的研究

虚拟现实技术具有广阔应用前景,但它对大脑信息处理及认知影响尚不清楚。本文结合头皮脑电,设计了平面和虚拟现实两种模式的视觉任务,对比分析虚拟现实对事件相关电位（ERP）的影响,探索沉浸式视觉体验过程中大脑的认知加工过程。参考电极的选择是研究事件相关电位的关键,为获得更客观的脑电信息,本文首先使用参考电极标准化技术将记录的脑电信号的参考近似转换为理想的零点,经过必要的预处理,再采用叠加平均的方法从中提取ERP。结果发现,与平面模式相比,在虚拟现实模式下ERP成分中没有出现显著的P300成分,这可能与大脑产生疲劳感相关;虚拟现实模式下N100成分的潜伏期提前、幅值增大,反映了虚拟现实环境更容易引起注意,并使人产生沉浸感。      

基于PCA和自适应步长IVA的EMG伪迹去除方法

针对脑电(EEG)信号在采集过程中易受到肌电(EMG)伪迹干扰,且EMG伪迹复杂多变难以去除的问题,提出一种基于主分量分析(PCA)和自适应步长独立向量分析(IVA)相结合的EEG中EMG伪迹去除方法。首先,利用PCA将EEG信号的主分量提取出来,对数据降维;然后对主分量引入IVA算法,根据高阶统计量和二阶统计量,结合EMG伪迹的非高斯性和弱相关性进行EMG伪迹分离,同时引入基于分离效果的自适应步长选取方法,增强分离效果。实验中采集了8通道的EEG信号,测得各通道相对均方根误差为0.09～0.2,算法的平均EMG伪迹分离率为98%,且相比单独使用IVA时间节省20%,该方法适用于动态EEG中EMG伪迹的去除。     

单通道脑电信号中眼电干扰的自动分离方法

当前主流的眼电(EOG)去除方法需要利用多通道脑电的相关性,难以在单通道的便携式脑机接口(BCI)中应用。该文提出一种基于长时差分振幅包络与小波变换的眼电干扰自动分离方法。首先在原脑电信号的长时差分振幅包络上实施双门限法来精确检测眼电的起止点,然后利用sym5小波对脑电进行分解并引进Birg_Massart策略来自适应地确定小波重构系数阈值,最后通过小波重构精确地估计眼电,实现单通道上眼电与脑电的自动分离。大量实验证明,该方法与主流的平均伪迹回归分析和基于独立成分分析(ICA)的方法相比,能够获得更好的估计眼电与原眼电的相关性,保证更高的校正信噪比和较强的实时性,能够满足脑机接口多方面的需要。     

Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEG/EEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal.      

应用形态学的自适应门限干扰检测算法

该文针对通信信号中背景噪声复杂的问题,应用数字形态学的信号预处理方法,能较好地滤除背景噪声;又由于单一门限值难以实现对不同宽度干扰的检测,提出一种应用形态学自适应门限的干扰检测算法。此算法首先对信号谱线进行功率谱估计,然后利用形态学的方法进行预处理,再根据信号功率谱的分布情况,选取不同的门限值,实现门限的自适应,为检测不同占有用信号带宽大小的窄带干扰提供了有效的方法。该文提出的方法不会受噪底变化的影响,计算量小,复杂度较低,适用于星上卫星通信的实时频谱监测。经过Matlab仿真实验得出,当采用结构元素长度为25的扁平型结构元素时,通过形态学中的膨胀预处理方法以及自适应门限可以得到检测效果比传统的连续均值去除算法(CME)算法有6dB以上的提升。      

改进的EEMD算法在时域航空电磁信号降噪中的研究

常规降噪方法在应用于时域航空电磁信号降噪时需根据噪声情况人为进行参数调整,自适应性较差。总体经验模态分解（EEMD）算法对非线性、非平稳信号处理具有良好的自适应特性,传统的EEMD算法进行噪声抑制是将高频本征模态分量滤除,将低频分量重构得到降噪信号,这种方法易失掉高频分量中的有效信号。本文提出一种改进的EEMD降噪算法,应用于时域航空电磁信号的处理。该方法结合时域航空电磁信号的衰减特性,将信号EEMD分解后得到本征模态分量,其中包含信号和噪声,经Savitzky Golay平滑滤波,再将高频部分进行阈值去噪,最后得到干净的本征模态分量进行重构。实验结果表明在输入信号信噪比小于等于15 dB的情况下,输出信噪比能够提高12 dB左右,在抑制噪声的同时保留了更多有效信息。      

Adaptive Threshold Median Filter for Multiple-Impulse Noise

Attenuating the noises plays an essential role in the image processing. Almost all the traditional median filters concern the removal of impulse noise having a single layer, whose noise gray level value is constant. In this paper, a new adaptive median filter is proposed to handle those images corrupted not only by single layer noise. The adaptive threshold median filter （ATMF） has been developed by combining the adaptive median filter （AMF） and two dynamic thresholds. Because of the dynamic threshold being used, the ATMF is able to balance the removal of the multiple-impulse noise and the quality of image. Comparison of the proposed method with traditional median filters is provided. Some visual examples are given to demonstrate the performance of the proposed filter.     

An Adaptive Radial Basis Function Neural Network Filter for Noise Reduction in Biomedical Recordings

Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle and baseline, severely limiting its utility. Artificial neural networks (ANNs) are effective and powerful tools for removing interference from EEGs. Several methods have been developed, but ANNs appear to be the most effective for reducing muscle and baseline contamination, especially when the contamination is greater in amplitude than the brain signal. An ANN as a filter for EEG recordings is proposed in this paper, developing a novel framework for investigating and comparing the relative performance of an ANN incorporating real EEG recordings. This method is based on a higher-order statistics-based radial basis function (RBF) network. This ANN improves the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. Average results for the RBF-based method provided a noise reduction (SIR) of (mean\(\pm \) SD) \(\mathrm{SIR}=19.3\pm 0.3\) in contrast to traditional compared methods that, for the best case, yielded \(\mathrm{SIR}=15.2\pm 0.3\). The system has been evaluated within a wide range of EEG signals. The present study introduces a new method of reducing all EEG interference signals in one step with low EEG distortion and high noise reduction.     

Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data

Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye blinks and electrical noise, etc., is an important problem in EEG signal processing research. These artifacts must be corrected before further analysis because it renders subsequent analysis very error-prone. One solution is to reject the data segment if artifact is present during the observation interval, however, the rejected data segment could contain important information masked by the artifact. The independent component analysis (ICA) can be an effective and applicable method for EEG denoising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ concept of the spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any cerebral activity from the extracted-artifacts ICs, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as it is not necessary to identify all ICs. Computer experiments are carried out, which demonstrate effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.     

基于噪声分布特性的脉冲信号自适应门限检测

通过分析数字脉冲信号幅度差分的概率分布模型，得到噪声的分布特性，并推导出用于脉冲信号检测的时域自适应门限。该门限可通过计算信号幅度差分绝对值的均值直接得到。该门限算法的第一个优点是门限值仅与噪声统计特性相关，与信号无关，可避免传统自适应算法中检测门限被强信号拉高，造成强信号附近的弱信号漏检问题;第二个优点是计算复杂度低，非常适合于实时性要求高而资源不足的现场可编程逻辑门阵列（FPGA）等硬件平台实现，为脉冲信号的实时捕获和后续的侦察处理提供支撑。     

Enhanced ${mu }$ Rhythm Extraction Using Blind Source Separation and Wavelet Transform

The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.     

Using an eye tracker for accurate eye movement artifact correction

We present a new method to correct eye movement artifacts in electroencephalogram (EEG) data. By using an eye tracker, whose data cannot be corrupted by any electrophysiological signals, an accurate method for correction is developed. The eye-tracker data is used in a Kalman filter to estimate which part of the EEG is of ocular origin. The main assumptions for optimal correction are summed and their validity is proven. The eye-tracker-based correction method is objectively evaluated on simulated data of four different types of eye movements and visually evaluated on experimental data. Results are compared to three established correction methods: Regression, Principal Component Analysis, and Second-Order Blind Identification. A comparison of signal to noise ratio after correction by these methods is given in Table II and shows that our method is consistently superior to the other three methods, often by a large margin. The use of a reference signal without electrophysiological influences, as provided by an eye tracker, is essential to achieve optimal eye movement artifact removal.