基于Flexible ICA的睡眠EEG及EOG中心电伪差的消除

提出了一种采用自适应非线性函数的ICA学习算法,Flexible ICA算法,并将其应用于睡眠EEG自动分期的前期预处理中,用于消除采集到的各通道信号中的心电伪差.实验结果证明,Flexible ICA算法能够快速有效的消除各通道的心电伪差,为后期的睡眠EEG自动分期打下了良好的基础.     

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。     

Mean field model of acetylcholine mediated dynamics in the thalamocortical system

A recent continuum model of the large scale electrical activity of the thalamocortical system is generalized to include cholinergic modulation. The model is examined analytically and numerically to determine the effect of acetylcholine (ACh) on its steady states, linear stability, spectrum, and temporal responses. Changing the ACh concentration moves the system between zones of one, three, and five steady states, showing that neuromodulation of synaptic strength is a possible mechanism by which multiple steady states emerge in the brain. The lowest firing rate steady state is always stable, and subsequent fixed points alternate between stable and unstable. Increasing ACh concentration changes the form of the spectrum. Increasing the tonic level of ACh concentration increases the magnitudes of the N100 and P200 in the evoked response potential (ERP), without changing the timing of these peaks. Driving the system with a pulse of cholinergic activity results in a transient increase in the firing rate of cortical neurons that lasts over . Step-like increases in cortical ACh concentration cause increases in the firing rate of cortical neurons, with rapid responses due to fast acting nicotinic receptors and slower responses due to muscarinic receptor suppression of intracortical connections.     

The dynamics of EEG entropy

The scaling properties of human EEG have so far been analyzed predominantly in the framework of detrended fluctuation analysis (DFA). In particular, these studies suggested the existence of power-law correlations in EEG. In DFA, EEG time series are tacitly assumed to be made up of fluctuations, whose scaling behavior reflects neurophysiologically important information and polynomial trends. Even though these trends are physiologically irrelevant, they must be eliminated (detrended) to reliably estimate such measures as Hurst exponent or fractal dimension. Here, we employ the diffusion entropy method to study the scaling behavior of EEG. Unlike DFA, this method does not rely on the assumption of trends superposed on EEG fluctuations. We find that the growth of diffusion entropy of EEG increments of awake subjects with closed eyes is arrested only after approximately 0.5 s. We demonstrate that the salient features of diffusion entropy dynamics of EEG, such as the existence of short-term scaling, asymptotic saturation, and alpha wave modulation, may be faithfully reproduced using a dissipative, first-order, stochastic differential equation—an extension of the Langevin equation. The structure of such a model is utterly different from the “noise+trend” paradigm of DFA. Consequently, we argue that the existence of scaling properties for EEG dynamics is an open question that necessitates further studies.     

不同思维作业电信号的高准确度模式分类

本文采用独立分量(ICA)分析对不同思维作业的脑电(EEG)信号进行预处理,再用自回归(AR)参数模型提取EEG信号特征,最后利用BP网络完成对特征样本集的训练和分类。实验结果表明,所采用的方法提高了脑电思维模式作业的准确度,对两种到五种不同思维作业未经训练的数据的平均分类准确度达到79％以上,超过现有文献报道的结果。     

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。     

Hidden pattern discovery on event related potential EEG signals

EEG signals are important to capture brain disorders. They are useful for analyzing the cognitive activity of the brain and diagnosing types of seizure and potential mental health problems. The Event Related Potential can be measured through the EEG signal. However, it is always difficult to interpret due to its low amplitude and sensitivity to changes of the mental activity. In this paper, we propose a novel approach to incrementally detect the pattern of this kind of EEG signal. This approach successfully summarizes the whole stream of the EEG signal by finding the correlations across the electrodes and discriminates the signals corresponding to various tasks into different patterns. It is also able to detect the transition period between different EEG signals and identify the electrodes which contribute the most to these signals. The experimental results show that the proposed method allows the significant meaning of the EEG signal to be obtained from the extracted pattern.     

海马参与工作记忆目标匹配增强及其时间过程——基于颅内脑电的研究

已有的研究结果表明,海马参与记忆的编码和提取,并且它会受到新发生事件与已存储记忆匹配或者不匹配的影响.先前的功能磁共振研究报道,在延迟匹配任务中,目标匹配增强作为一种工作记忆成分,与物体性质和位置的整合有关,能够显著地激活海马体部.但是,关于这一过程的时间信息目前尚不清楚.本研究利用特定癫痫病人在双侧海马植入的深部电极,跨被试间电极触点位置基本一致,因此具有较高的空间分辨率和时间分辨率的优势.我们发现,左侧海马体部在目标匹配增强中起着重要作用.同时,这种效应发生在探测刺激出现后600～650 ms,大约在知觉匹配增强或者P300等知觉效应后200 ms.另外,对于每一个被试,目标匹配增强的潜伏期与平均反应时成正相关.结果揭示,当工作记忆的任务与性质-位置捆绑有关时,海马参与并起重要作用.结果说明,目标匹配增强效应在知觉过程之后发生,表明了工作记忆不同成分在海马的分离.     

小波相干分析及其在听觉与震动刺激事件相关电位处理中的应用

尝试将小波相干方法应用于事件相关电位实验的脑电信号分析中。实验分为三组：听觉任务、震动任务1和震动任务2。对12个受试者的实验数据进行40Hz左右的小波相干分析,计算了前额脑区与其他各脑区之间的相干性,发现震动任务的小波相干值大于听觉任务并有显著差异,且在不同的任务中,各脑区的小波相干值有其明显不同的分布特征,且随时间呈有规律的变化。分析体现了小波相干在短时脑电信号处理上的优势。     

Can back-projection fully resolve polarity indeterminacy of independent component analysis in study of event-related potential?

In the study of event-related potentials (ERPs) using independent component analysis (ICA), it is a traditional way to project the extracted ERP component back to electrodes for correcting its scaling (magnitude and polarity) indeterminacy. However, ICA tends to be locally optimized in practice, and then, the back-projection of a component estimated by the ICA can possibly not fully correct its polarity at every electrode. We demonstrate this phenomenon from the view of the theoretical analysis and numerical simulations and suggest checking and modifying the abnormal polarity of the projected component in the electrode field before further analysis. Moreover, when several components are to be projected, instead of the parallel projection of those components simultaneously, the sequential projection of component by component permits the correction of the abnormal polarity of a certain projected component at a certain electrode, which can improve the accuracy of the back-projection. Furthermore, after one extracted component by the ICA is projected back to electrodes under the global optimization, we cannot achieve the real source yet, but the determined scaled source, i.e., the multiplication between the real source and the mapping coefficient from the source to the point at the scalp.     

Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs

The dynamic behavior of scalp potentials (EEG) is apparently due to some combination of global and local processes with important top-down and bottom-up interactions across spatial scales. In treating global mechanisms, we stress the importance of myelinated axon propagation delays and periodic boundary conditions in the cortical-white matter system, which is topologically close to a spherical shell. By contrast, the proposed local mechanisms are multiscale interactions between cortical columns via short-ranged non-myelinated fibers. A mechanical model consisting of a stretched string with attached nonlinear springs demonstrates the general idea. The string produces standing waves analogous to large-scale coherent EEG observed in some brain states. The attached springs are analogous to the smaller (mesoscopic) scale columnar dynamics. Generally, we expect string displacement and EEG at all scales to result from both global and local phenomena. A statistical mechanics of neocortical interactions (SMNI) calculates oscillatory behavior consistent with typical EEG, within columns, between neighboring columns via short-ranged non-myelinated fibers, across cortical regions via myelinated fibers, and also derives a string equation consistent with the global EEG model.     

Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite–protein networks and improve pattern recognition for multiple biomarker selection

A novel approach is presented combining quantitative metabolite and protein data and multivariate statistics for the analysis of time-related regulatory effects of plant metabolism at a systems level. For the analysis of metabolites, gas chromatography coupled to a time-of-flight mass analyzer (GC-TOF-MS) was used. Proteins were identified and quantified using a novel procedure based on shotgun sequencing as described recently (Weckwerth etal., 2004b, Proteomics 4, 78–83). For comparison, leaves of Arabidopsis thaliana wild type plants and starchless mutant plants deficient in phosphoglucomutase activity (PGM) were sampled at intervals throughout the day/night cycle. Using principal and independent components analysis, each dataset (metabolites and proteins) displayed discrete characteristics. Compared to the analysis of only metabolites or only proteins, independent components analysis (ICA) of the integrated metabolite/protein dataset resulted in an improved ability to distinguish between WT and PGM plants (first independent component) and, in parallel, to see diurnal variations in both plants (second independent component). Interestingly, levels of photorespiratory intermediates such as glycerate and glycine best characterized phases of diurnal rhythm, and were not influenced by high sugar accumulation in PGM plants. In contrast to WT plants, PGM plants showed an inversely regulated cluster of N-rich amino acid metabolites and carbohydrates, indicating a shift in C/N partitioning. This observation corresponds to altered utilization of urea cycle intermediates in PGM plants suggesting enhanced protein degradation and carbon utilization due to growth inhibition. Among the proteins chloroplastidic GAPDH (At3g26650) was the best discriminator between WT and PGM plants in contrast to the cytosolic isoform (At1g13440) according to the primary effect of mutation located in the chloroplast. The described method is applicable to all kinds of biological systems and enables the unbiased identification of biomarkers embedded in correlative metabolite–protein networks.     

Rhythmic Oscillations in Quantitative EEG Measured During a Continuous Performance Task

The objective of the present investigation was to determine if cyclic variations in human performance recorded during a 30 min continuous performance task would parallel cyclic variations in right-hemisphere beta-wave activity. A fast fourier transformation was performed on the quantitative electroencephalogram (qEEG) and the performance record of each participant (N = 62), producing an individual periodogram for each outcome measure. An average periodogram was then produced for both qEEG and performance by combining (averaging) the amplitudes associated with each periodicity in the 62 original periodograms. Periodicities ranging from 1.00 to 2.00 min and from 4.70 to 5.70 min with amplitudes greater than would be expected due to chance were retained (Smith et al. 2003). The results of the present investigation validate the existence of cyclic variations in human performance that have been identified previously (Smith et al. 2003) and extend those findings by implicating right-hemisphere mediated arousal in the process (Arruda et al. 1996, 1999, 2007). Significant cyclic variations in left-hemisphere beta-wave activity were not observed. Taken together, the findings of the present investigation support a model of sustained attention that predicts cyclic changes in human performance that are the result of cyclic changes in right-hemisphere arousal.

Neurophysiological Age Differences During Task-Performance in a Stereoscopic Virtual Environment

In today’s society, there is an increasing number of workplaces in virtual environments (VE). But, there are only a few reports dealing with occupational health issues or age effects. The question arises how VR generally interferes with cognitive processes. This interference might have relevant implications for workability and work-efficiency in virtual environments. Event-related potentials are known to reflect different stages of stimulus reception, evaluation, and response. We have established an electroencephalographic (EEG) monitoring, focussing on event-related potentials (N100; mismatch negativity, i.e., MMN) to obtain access to attention dependent and pre-attentive processing of sensory stimuli applied in VE. The MMN is known to be correlated with the ability of subjects to react to an unexpected event. The aim of the present study was to investigate cognitive responses to distracting auditory stimuli in two different age groups in a virtual environment (VE) and in a real environment (“real reality”), and to compare characteristic neurophysiological response patterns. Data show that stimulus detection as given by the N100 amplitude and latency does not differ in both age groups and task conditions. In contrast, the pre-attentive processing as given by the MMN is altered in the VR such as the non-VR condition in an age-related manner. A relevant finding of the present study was that the age related differences seen in the non-VR condition were not strengthened in VR.