多通道脑电信号的盲分离

提出一种新的多通道脑电信号盲分离的方法,将小波变换和独立分量分析(independent component analysis,ICA)相结合,利用小波变换的滤噪作用,将混合在原始脑电的部分高频噪声滤除后,再重构原始脑电作为ICA的输入信号,有效地克服了现有ICA算法不能区分噪声的缺陷。实验结果表明,该方法对多通道脑电的盲分离是很有效的。     

空间独立成分分析实现fMRI信号的盲分离

独立成分分析（ICA）在功能核磁共振成像（fMRI)技术中的应用是近年来人们关注的一个热点。简要介绍了空间独立成分分析（SICA）的模型和方法,将fMRI信号分析看作是一种盲源分离问题,用快速算法实现fMRI信号的盲源分离。对fMRI信号的研究大多是在假定已知事件相关时间过程曲线的情况下,利用相关性分析得到脑的激活区域。在不清楚有哪几种因素对fMRI信号有贡献、也不清楚其时间过程曲线的情况下,用SICA可以对fMRI信号进行盲源分离,提取不同独立成分得到任务相关成分、头动成分、瞬时任务相关成分、噪声干扰、以及其它产生fMRI信号的多种源信号。     

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(IndependentComponentAnalysis,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。     

光泵磁强计双轴探测听觉诱发脑磁信号的初步探索

目的 光泵磁强计（optically pumped magnetometer，OPM）脑磁图（magnetoencephalography，MEG）作为新一代脑功能成像技术，在多轴探测设计下具备了对传统MEG信号“盲区”的检测能力，为脑内功能活动研究提供更全面的技术工具。本文旨在探讨双轴OPM-MEG在测量真实生理反应时的信号分布差异特征。方法 采用9通道OPM-MEG对10名健康受试者的听觉相关频率跟随反应进行检测。通过每个被试1 000个试次的数据采集，获取所有通道头皮切向（Y轴）和径向（Z轴）的磁响应信号。结果 研究显示，双轴OPM-MEG记录到的信号在强度和分布上与传统MEG存在明显区别。双轴信号明显强于传统MEG，且传统MEG难以记录的切向信号显著强于径向信号。结论 本研究证实了双轴OPM-MEG在测量真实生理信号方面的能力，并且双轴测量能够获取更丰富的信息，特别是可能存在于传统MEG测量盲区中的脑功能活动信号。这为基于传统MEG记录的神经电活动模型带来了重大更新。双轴OPM-MEG的多轴记录特性在脑科学研究和脑疾病诊断领域都具有巨大的应用潜力。这项研究初步展示了双轴OPM-MEG在听觉诱发信号研究中的价值，为后续深入研究奠定了基础。     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

脑磁图的临床应用研究

脑磁图 (magnetoencephalogragphy,MEG) ,是一种通过测量脑磁场信号 ,对脑功能区进行定位及评价其状态的新技术 ,具有对人体无侵袭、无损伤等特点 ,目前已在人脑的功能研究和临床上进行应用。脑磁图临床的应用研究1　MEG的基础研究MEG可用于听觉、视觉、语言、运动、脑细胞信息     

基于独立元分析的脑磁图源定位

在脑磁图源定位问题中,通常感兴趣的是脑内众多神经活动源中的一个或几个,而传统源定位方法,如多信号分类方法,需要将所有源的位置都确定后,通过重组信号波形才能获得所感兴趣源的位置信息。为了提高定位效率,文章作者提出了一种结合独立元分析的脑磁图源定位方法。实验结果表明,该方法能加快定位的速度,同时能够在一定程度上克服噪声的影响,具有更强的抗噪声能力。     

基于小波变换的单次诱发电位信号时频分析

比较小波变换和平均叠加两种方法提取“模拟自然阅读”刺激模式下的诱发脑电信号,分析其时频特性,并进行脑功能源分布定位分析。结果显示,采用平均叠加法来提取和分析诱发电位信号,损失了某些重要的诱发电位成分,且其功能源分布定位反映的只是等效功能源的静态过程;而使用小波变换和脑功能源定位来提取和分析单次诱发电位信号,既能观察到丰富的诱发电位成分,又能反映脑功能源的实时动态活动过程。这表明,小波变换下的时频分析是脑电信号处理的一种可行的新方法。     

多通道神经信号分析方法及其应用

在神经科学研究中,多通道记录方法被普遍应用在对神经元群体活动特性的研究中.通过分析多个神经元的活动,可以了解神经系统协同编码外界信息的规则以及大脑实现各项功能的机制.为了挖掘出多通道神经信号中携带的信息及其潜在的相关性,需要合适的计算方法辅助对神经元放电活动进行解码.本文回顾了多通道神经信号分析中的一些常见方法,以及它...     

海马区锥体神经元轴突高频电刺激对胞体的影响

目的 深部脑刺激（deep brain stimulation,DBS）利用持续的电脉冲高频刺激（high-frequency stimulation,HFS）调控神经元的活动，可望用于治疗更多脑疾病。为了深入了解HFS的作用机制，促进DBS的发展，本文研究轴突HFS在引起轴突阻滞期间神经元胞体的改变。方法 在麻醉大鼠海马CA1区的锥体神经元轴突上施加脉冲频率为100 Hz的1 min逆向高频刺激（antidromic high-frequency stimulation,A-HFS）。为了研究胞体的响应，利用线性垂直排列的多通道微电极阵列，记录刺激位点上游CA1区锥体神经元胞体附近各结构分层上的诱发电位，包括A-HFS脉冲诱发的逆向群峰电位（antidromic population spike,APS）以及A-HFS期间施加的顺向测试脉冲诱发的顺向群峰电位（orthodromic population spike,OPS），并计算诱发电位的电流源密度（current-source density,CSD），用于分析A-HFS期间锥体神经元胞体附近动作电位的生成和传导。结果 锥体神经...     

Selecting EEG components using time series analysis in brain death diagnosis

In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient’s brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts. This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in brain death diagnosis, such automatic component selection is helpful.     

Two Distinct Dynamic Modes Subtend the Detection of Unexpected Sounds

The brain response to auditory novelty comprises two main EEG components: an early mismatch negativity and a late P300. Whereas the former has been proposed to reflect a prediction error, the latter is often associated with working memory updating. Interestingly, these two proposals predict fundamentally different dynamics: prediction errors are thought to propagate serially through several distinct brain areas, while working memory supposes that activity is sustained over time within a stable set of brain areas. Here we test this temporal dissociation by showing how the generalization of brain activity patterns across time can characterize the dynamics of the underlying neural processes. This method is applied to magnetoencephalography (MEG) recordings acquired from healthy participants who were presented with two types of auditory novelty. Following our predictions, the results show that the mismatch evoked by a local novelty leads to the sequential recruitment of distinct and short-lived patterns of brain activity. In sharp contrast, the global novelty evoked by an unexpected sequence of five sounds elicits a sustained state of brain activity that lasts for several hundreds of milliseconds. The present results highlight how MEG combined with multivariate pattern analyses can characterize the dynamics of human cortical processes.     

Coupled oscillators for modeling and analysis of EEG/MEG oscillations.

This study presents three EEG/MEG applications in which the modeling of oscillatory signal components offers complementary analysis and an improved explanation of the underlying generator structures. Coupled oscillator networks were used for modeling. Parameters of the corresponding ordinary coupled differential equation (ODE) system are identified using EEG/MEG data and the resulting solution yields the modeled signals. This model-related analysis strategy provides information about the coupling quantity and quality between signal components (example 1, neonatal EEG during quiet sleep), allows identification of the possible contribution of hidden generator structures (example 2, 600-Hz MEG oscillations in somatosensory evoked magnetic fields), and can explain complex signal characteristics such as amplitude-frequency coupling and frequency entrainment (example 3, EEG burst patterns in sedated patients).     

Combined mapping of human auditory EEG and MEG responses

Auditory electric and magnetic P50(m), N1(m) and MMN(m) responses to standard, deviant and novel sounds were studied by recording brain electrical activity with 25 EEG electrodes simultaneously with the corresponding magnetic signals measured with 122 MEG gradiometer coils. The sources of these responses were located on the basis of the MEG responses; all were found to be in the supratemporal plane. The goal of the present paper was to investigate to what degree the source locations and orientations determined from the magnetic data account for the measured EEG signals. It was found that the electric P50, N1 and MMN responses can to a considerable degree be explained by the sources of the corresponding magnetic responses. In addition, source-current components not detectable by MEG were shown to contribute to the measured EEG signals.     

Decomposition of Neurological Multivariate Time Series by State Space Modelling

Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison.     

On the application of (topographic) independent and tree-dependent component analysis for the examination of DCE-MRI data

In this contribution we investigate the applicability of different methods from the field of independent component analysis (ICA) for the examination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data from breast cancer research. DCE-MRI has evolved in recent years as a powerful complement to X-ray based mammography for breast cancer diagnosis and monitoring. In DCE-MRI the time related development of the signal intensity after the administration of a contrast agent can provide valuable information about tissue states and characteristics. To this end, techniques related to ICA, offer promising options for data integration and feature extraction at voxel level. In order to evaluate the applicability of ICA, topographic ICA and tree-dependent component analysis (TCA), these methods are applied to twelve clinical cases from breast cancer research with a histopathologically confirmed diagnosis. For ICA these experiments are complemented by a reliability analysis of the estimated components. The outcome of all algorithms is quantitatively evaluated by means of receiver operating characteristics (ROC) statistics whereas the results for specific data sets are discussed exemplarily in terms of reification, score-plots and score images.     

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4×5×7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.     

Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA

Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.