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。     

经颅磁刺激同步脑电活动伪迹去除方法的研究进展

经颅磁刺激同步脑电( TMS-EEG)技术是研究大脑功能网络的有效手段,但TMS过程中诱发的伪迹一直是阻碍TMS-EEG技术发展的瓶颈.阐述了TMS和EEG技术结合产生伪迹的原因,从伪迹的来源入手,就TMS放电伪迹、肌电伪迹、听觉伪迹及残留伪迹等方面,总结了近十几年来文献中提到的伪迹去除方法,并对相关技术的未来发展作了...     

基于高阶累积量算法的脑电信号中心电伪迹的消除

在脑电信号测量过程中，不可避免的会存在心电信号的干扰，给医生的诊断带来困难。本文将盲源分离理论用于研究脑电信号中的心电伪迹消除，介绍了盲源分离问题的基本模型、基于高阶累积量的独立性判决准则以及联合近似对角化算法。仿真实验表明。该方法能有效去除脑电信号中的心电伪迹干扰。     

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。     

结合QRS检测和小波阈值的膈肌肌电信号降噪方法

为了消去夹杂在膈肌肌电(EMGdi)信号中的心电干扰,在比例阈值算法的基础上,提出一种结合QRS检测和小波阈值的降噪方法.首先,根据小波系数的相关性构造QRS波群的检测方法,分析确定干扰的位置和范围;其次,将小波系数分为受干扰和未受干扰两部分,并构造相应的阈值算法,针对性地处理受干扰系数,以未受干扰部分系数作为阈值算法构造的依据;最后,重构处理后的小波系数,得到降噪后的EMGdi信号.对临床采集信号的处理对比表明,该方法能够更为有效地去除心电干扰,并更好地保留EMGdi的有用信号.     

基于非线性尺度小波变换的表面肌电信号的分类

表面肌电信号（Surface EMG,sEMG）是一种复杂的非线性非平稳信号。我们介绍了一种非线性尺度小波变换（Wavelet transform with nonlinear scale，NWT）。由于NWT具有渐进缩短时间分辨率的特点．所以有利于从sEMG信号获得精确的时一频信息。首先，用NWT将sEMG信号（30组前臂内旋和30组外旋的sEMG信号）变换为强度分布（时频分布）．然后，用由主成分分析获得的强度分布特征值构成特征向量．最后，用BP神经网络对两种信号模式的特征向量进行分类识别。结果表明：与两种传统的时频分析方法相比，NWT能够获得较高的正确识别率．同时降低了神经网络计算的复杂度。     

机器学习与脑电信号分析相结合的眩晕状态分类

背景：脑电图是临床上检测及分析眩晕的一种常用手段,目前多采用单极或多级导联描记并分析脑电频率是否异常。但眩晕的脑电活动过程是异常复杂的,仅采用频率快慢分析的方法,很难对眩晕状态进行准确的分类和检测。目的：将机器学习与脑电信号分析相结合对眩晕状态进行分类,这对眩晕的诊断具有一定的研究意义和临床应用价值。方法：采用无创的前庭功能调节技术前庭电刺激制造可逆的眩晕状态,刺激电流强度为1,2,4倍皮肤感知阈值,被试在不同强度电流刺激后需填写眩晕残障量表,根据眩晕障碍量表评估结果将眩晕症状分为不同的等级,以此作为脑电分类有监督学习的数据标签。采集刺激后的脑电信号,通过小波变换提取脑电信号的小波能量以及小波熵的样本特征,利用多种机器学习分类模型对有无眩晕以及不同等级眩晕的样本特征进行分类。结果与结论：（1）通过对多种分类模型分类结果的对比发现：基于脑电信号小波变换特征的有监督学习分类可以实现是否眩晕和眩晕等级的二分类和多分类;（2）随机森林分类模型较逻辑回归模型、支持向量机模型、反向传播神经网络模型在眩晕检测的二分类以及多分类问题上表现出较高的准确率,其中二分类准确率最高可达82.5%,操作特性曲线...     

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.     

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.     

An Ocular Artifacts Removal Method Based on Canonical Correlation Analysis and Two-Channel EEG Recordings

In order to more effectively apply an artifact removal method in an online brain-computer interface(BCI) system, a new method based on canonical correlation analysis(CCA) and two-channel electroencephalography(EEG) recordings to quickly remove ocular artifacts (OA) is proposed in this paper. Considering both the formation of EEG signals contaminated by OA and the spread of OA, vertical electrooculography (VEOG) was appropriately introduced in CCA, and the blind source separation (BSS) method based on CCA was used in a new way during the OA removal process. Both experimental and comparison with ICA and SOBI results show that the new method with simple calculation and fast processing speed can effectively separate and remove OA using only two-channel EEG recordings, with retaining useful EEG signals. Hence, this method used in an online BCI system will be more effective.     

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.     

基于独立分量分析的脑电中眼电伪迹消除

利用独立分最分析的方法对脑电中眼电伪迹成分进行剔除。针对扩腮熵最大算法能够同时分离超高斯和亚高斯信号的特点，将脑电信号分解成独立分量，利用伪迹脑地形图的特征，将伪迹分最分离，得到不含伪迹的脑电信号。实验结果表明。该算法具有较强的稳健性和实用性。     

基于联合近似对角化算法的脑电图中眼电伪迹消除

利用联合近似对角化(JADE)算法对脑电图中眼电伪迹成分进行剔除.针对JADE算法能够同时分离超高斯和亚高斯信号的特点,将脑电图信号分解成独立分量,利用伪迹脑地形图的特征,将伪迹分量分离,得到不含伪迹的脑电图信号.实验结果表明,该算法具有较强的稳健性和实用性.     

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。     

Removal of ocular artifacts from the REM sleep EEG.

The present report concerns the first study in which electrooculographic (EOG) contamination of electroencephalographic (EEG) recordings in rapid eye movement (REM) sleep is systematically investigated. Contamination of REM sleep EEG recordings in six subjects was evaluated in the frequency domain. REM-active and REM-quiet series were obtained for each subject. Transfer coefficients and power spectra of EOG and EEG indicated that (a) increases in transfer coefficients beyond 4.5 Hz are brought about by residual EEG in the EOG, and (b) EOG-EEG contamination in the delta band is most pronounced in frontal, intermediate in central and negligible in occipital leads. It was found that correction of the REM-active series resulted in significant (c) reductions in power, (d) increases in interhemispheric coherences and (e) reductions in degree of lateral asymmetry. These effects were largest for frontal leads, but still marked for central ones. The results are discussed in the light of previous findings concerning models of hemispheric functioning during REM sleep.