MEG and TMS combined with EEG for mapping alcohol effects.

Magnetoencephalography (MEG) is a noninvasive method of studying magnetic fields from outside the skull that are generated by at least partially synchronized neuronal populations in the brain. The advantage of MEG over electroencephalography (EEG) is the transparency of the skull, scalp, and brain tissue to the magnetic fields, which facilitates easy localization of the cortical activity. In MEG, alcohol increased the relative power of the alpha rhythm and reduced the relative power of beta activity in parieto-occipital regions. In contrast, no changes were observed in EEG, indicating that these methods differently detect alcohol's action on the cortex. Furthermore, MEG and EEG also differently detected the effects of alcohol on cognition. Alcohol reduced magnetic and electric auditory N1 and mismatch negativity amplitudes. P3a amplitudes were also reduced in EEG but not in MEG, suggesting that different cortical areas are responsible for alcohol's action on involuntary attention. Transcranial magnetic stimulation (TMS) provides new possibilities for studying localized changes in the electrical properties of the human cortex, especially when combined with EEG. Different cortical areas can be stimulated and the subsequent brain activity can be measured, yielding information about cortical excitability and connectivity. Alcohol modulates EEG responses evoked by motor-cortex TMS, the effects being largest at the right prefrontal cortex (assessed by minimum-norm estimation), meaning that alcohol changed the functional connectivity between motor and prefrontal cortices. Furthermore, alcohol decreases amplitudes of EEG responses after the left prefrontal stimulation of anterior parts of the cortex, which may be associated with the decrease of prefrontal cortical excitability. Taken together, MEG and TMS combined with EEG provide new insight into the focal actions of alcohol on the cortex with a temporal resolution of milliseconds, giving information different from that given by other brain imaging modalities.     

A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data

Magnetoencephalography (MEG) and electroencephalography (EEG) sensor measurements are often contaminated by several interferences such as background activity from outside the regions of interest, by biological and non-biological artifacts, and by sensor noise. Here, we introduce a probabilistic graphical model and inference algorithm based on variational-Bayes expectation-maximization for estimation of activity of interest through interference suppression. The algorithm exploits the fact that electromagnetic recording data can often be partitioned into baseline periods, when only interferences are present, and active time periods, when activity of interest is present in addition to interferences. This algorithm is found to be robust and efficient and significantly superior to many other existing approaches on real and simulated data.     

基于独立成分分析和互相关分析的脑功能连接研究

功能相关的脑区存在功能连接,利用静息状态功能磁共振成像(functional magnetic resonance imaging,fMRI)技术,联合独立成分分析(independent component analysis,ICA)和互相关(cross correlation analysis,CCA)分析方法检测人脑初级运动皮层功能连接网络。首先采用空间ICA定位初级运动皮层,作为感兴趣区域(region of interest,ROI);然后采用互相关分析方法检测静息状态大脑特定皮层的功能连接性。实验结果表明:此研究方法较好地检测了大脑运动系统的典型区域,包括初级运动区、运动前区、辅助运动区等,与解剖学、组织化学及生理学技术检测的结果相一致,证实了该方法在脑功能连接性研究方面是有效的。     

Modified models and simulations for estimating dynamic functional connectivity in resting state functional magnetic resonance imaging

As understanding the nature of brain networks through dynamic functional connectivity (dFC) estimation is of paramount significant, the introduction and revision of blood-oxygen-level dependent (BOLD) signal simulation methods in brain regions and dFC estimation methods have gained significant ground in recent years. Based on the observation of BOLD signals with multivariate nonnormal distribution in functional magnetic resonance imaging (fMRI) images, we first propose a copula-based method for the production of these signals, in which nonnormal data are generated with a selected time-varying covariance matrix. Therefore, we can compare the performance of models in the cases where brain signals have a multivariate nonnormal distribution. Then, two kendallized exponentially weighted moving average (KEWMA) and kendallized dynamic conditional correlation (KDCC) multivariate volatility models are introduced which are based on two well-known and commonly used exponentially weighted moving average (EMWA) and dynamic conditional correlation (DCC) models. The results show that KDCC model can estimate conditional correlation significantly far better than the former ones (ie, DCC, standardized dynamic conditional correlation, EWMA, and standardized exponentially weighted moving average) on both types of data (ie, multivariate normal and nonnormal). In the next step, the bivariate normal distribution in Iranian resting state fMRI data is confirmed by using statistical tests, and it is shown that the dynamic nature of FC is not optimally detected using prevalent methods. Two alternative Portmanteau and rank-based tests are proposed for the examination of conditional heteroscedasticity in data. Finally, dFC in these data is estimated by employing the KDCC model.     

State-space models for optical imaging

Measurement of stimulus-induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging of intrinsic signals, to gather data capturing underlying signals of interest in the brain. These data are usually corrupted by artifacts, complicating interpretation of the signal; in the context of optical imaging, two primary sources of corruption are the heartbeat and respiration cycles. We introduce a new linear state-space framework that uses the Kalman filter to remove these artifacts from optical imaging data. The method relies on a likelihood-based analysis under the specification of a formal statistical model, and allows for corrections to the signal based on auxiliary measurements of quantities closely related to the sources of contamination, such as physiological processes. Furthermore, the likelihood-based modeling framework allows us to perform both goodness-of-fit testing and formal hypothesis testing on parameters of interest. Working with data collected by our collaborators, we demonstrate the method of data collection in an optical imaging study of a cat's brain.     

基于运动想象的脑电信号特征提取与分类

目的：以在已知类别的2种运动想象任务下采集的EEG信号为训练样本,识别测试样本中的运动想象任务。方法：在频域范围内,采用AR模型功率谱估计法所得C3、C4通道的功率谱密度,确定ERD/ERS较明显的频率范围;在时域范围内,比较C3、C4通道信号的能量差异,确定ERD/ERS较明显的时间段。采用带通滤波和小波包分析的方法提取训练集想象运动过程中ERD/ERS生理现象较明显的节律信号,分别采用线性分类器、支持向量机（SVM）实现测试集运动想象脑电数据的分类。结果：分类最佳正确率为87.14%。结论：小波包分析法能够较准确地提取想象左、右手运动的脑电信号的本质特征,结合支持向量机实现较好的抗干扰能力和分类性能。     

基于能量法的脑机接口运动想象分类研究

针对脑机接口运动想象脑电信号的分类识别问题,提出了一种基于小波包分解的C3、C4二通道能量特征提取方法。该方法首先采用6阶的巴特沃斯带通滤波对二通道脑电信号进行降噪;然后采用Daubechies类小波函数对其进行5层分解,选择第四层CD4、第五层CD5的小波系数进行重构并抽取其能量特征;最后采用线性距离判别进行分类和使用Kappa系数进行分类衡量。利用BCI2008竞赛的标准数据BCICIV_2b_gdf进行验证,结果表明利用该方法可以较好地反映事件相关同步和事件相关去同步的特征,为BCI研究中事件相关电位的分类识别提供了有效的手段。     

Epileptic seizure detection in EEG signal using machine learning techniques

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.     

基于多变量相空间重构的癫痫脑电分析

目的：应用多变量相空间重构对分析脑电信号,获取癫痫脑电的非线性特征.方法：鉴于脑电（EEG）的高维混沌特性,通过多变量相空间重构分析方法,将大脑分为左右2个半区,分别以8个EEG导联作为重构样本进行非线性分析,可以得到线性区域,从而得到相关维数的估计值.结果：5例确诊癫痫患者的脑电分析结果基本一致,癫痫发作前、中、后期相关维数有明显变化,与对照组的结果差异显著.结论：多变量相空间重构法适用于对短时含噪声的时间序列进行分析,能够避免延迟时间和嵌入维数等参数的选择,得到更可靠的结果.     

一种适于实时滤除ECG工频干扰新方法

讨论几种常用数字滤波方法的优缺点，并在Ｌｅｖｋｏｖ滤波法的基础上，提出一种新的滤波方法。采用上述几种滤波法对５０多人实测的心电信号进行滤波，结果表明，新提出的滤波方法效果最好，可满足数字心电图机实时处理的需要。     

多模态人体生理电信号的3D电视健康评估系统

为了满足3D电视健康评估多信号测量要求,应用高性能的生物采集芯片来设计可实时地采集所需人体电信号的硬件系统。该系统主要是采用ARM11微控制器S3C6410控制脑电/眼电采集芯片RHA2116和心电采集芯片ADS1298,并将芯片采集的数据通过控制芯片的USB口传至上位机软件,并可实时地显示和保存实验数据,再应用Matlab软件对各信号的数据进一步处理,进而做出健康评估。同时,针对观看3D电视脑区变化的不同设计出3D电视评估专用的脑电极放置方法和实验数据处理方式,可有效地对多信号的数据进行层次性分析。     

Voxel-ICA for reconstruction of source signal time-series and orientation in EEG and MEG

In electroencephalography (EEG) and magnetoencephalography signal processing, scalar beamformers are a popular technique for reconstruction of the time-course of a brain source in a single time-series. A prerequisite for scalar beamformers, however, is that the orientation of the source must be known or estimated, whereas in reality the orientation of a brain source is often not known in advance and current techniques for estimation of brain source orientation are effective only for high signal-to-noise ratio (SNR) brain sources. As a result, vector beamformers are applied which do not need the orientation of the source and reconstruct the source time-course in three orthogonal (x, y, and z) directions. To obtain a single time-course, the vector magnitude of the three orthogonal outputs of the beamformer can be calculated at each time point (often called neural activity index, NAI). The NAI, however, is different from the actual time-course of a source since it contains only positive values. Moreover, in estimating the magnitude of the desired source, the background activity (undesired signals) in the beamformer outputs also become all positive values, which, when added to each other, leads to a drop in the SNR. This becomes a serious problem when the desired source is weak. We propose applying independent component analysis (ICA) to the orthogonal time-courses of a brain voxel, as reconstructed by a vector beamformer, to reconstruct the time-course of a desired source in a single time-series. This approach also provides a good estimation of dipole orientation. Simulated and real EEG data were used to demonstrate the performance of voxel-ICA and were compared with a scalar beamformer and the magnitude time-series of a vector beamformer. This approach is especially helpful when the desired source is weak and the orientation of the source cannot be estimated by other means.     

Synchronous automated analysis of a spirogram and cardiac intervalogram]

The paper deals with the functional diagnosis based on the synchronous analysis of a spirogram and a cardiac intervalogram (CIG) by evaluating respiratory sinus arrhythmia (RSA). A method based on the calculation of R-R interval differences in separate breathing cycles is used for automated reckoning of RSA. A band-pass filter is employed to separate out a respiratory constituent of CIG. The programmed algorithmic use of the method is based on a linear selective transformation that is a general procedure for morphological analysis of cardiac signals in real time.     

基于多变量相空间重构法的癫痫脑电研究

鉴于脑电(EEG)的高维混沌特性,通过多变量相空间重构分析方法,将大脑分为左右两个半区,分别以8个EEG导联作为重构样本进行非线性分析,可以得到线性区域,进而更好地得到相关维数的估算值.为了检验算法的可行性,我们先用低维的Lorenz系统从数据量的要求进行试算,然后从时间的遍历性上进一步验证,并将其应用于正常人和癫痫患者脑电的分析,得到高维数值.与其他研究者的结果进行比较,结果表明:多变量相空间重构法适用于短时含噪声的时间序列,能够避免延迟时间和嵌入维数等参数的选择,得到更可靠的结果.     

孤独症谱系障碍男童视觉相关脑区与全脑功能连通性特征

目的 探讨孤独症谱系障碍(ASD)儿童静息态下视觉脑区与全脑功能连接特征,并与症状进行关联性分析,以揭示ASD儿童视觉异常行为背后的脑功能连接特征.方法 收集黑龙江省孤独症定点康复机构招募的2018-2019年间进行康复训练的ASD男童(34例)和在哈尔滨市多家幼儿园公开招募的健康对照男童(29例)的功能磁共振(fMR...     

Topographic electroencephalographic studies in a hyperbaric environment--specific reference to high pressure nervous syndrome

Hyperbaric chamber dives at various equivalent depths below sea level, i.e. 7, 14, 19 and 31 atmosphere absolute (ATA) with helium-oxygen or helium-nitrogen-oxygen have been performed at the Japan Marine Science and Technology Center. A two-dimensional (topographic) display of the scalp EEG was used during simulated underwater experiments to determine; 1) Whether there are any characteristic EEG patterns in high pressure nervous syndrome (HPNS), 2) the relationship between the EEG changes and the compression rate, and 3) the relationship between the EEG changes and the characteristic signs and symptoms of HPNS. A two-way analysis of variance and a distribution analysis technique revealed that the topographic brain patterns depended on the diving depth and indicated the most affected brain areas during compression and decompression. Significant correlations between the diving depth and the EEG potentials were observed at different brain locations. Alpha waves showed a diffuse cortical distribution. Theta wave activity was more localized in the frontal midline region. These waves developed paroxysmally in relatively brief bursts supplanting or intermixing with normal background EEG rhythms. In our subjects, frontal midline theta activity was associated mostly with some of the characteristic features of HPNS, such as a transient episode of laughter or euphoria at depths greater than 21 ATA. An intimate correlation between frontal midline theta wave and laughter was observed. Frontal midline theta waves may be related to emotional activities induced by helium under high pressure. There were significant individual variations in susceptibility and subjective signs and symptoms. The EEG is of great value in studying man's physiological reactions in an undersea environment and also very important in selecting divers who are relatively more tolerant of a severe hyperbaric environment.     

孤独症谱系障碍男童初级听觉脑区与全脑功能连通性及与感觉行为的关联研究

目的 研究孤独症谱系障碍(ASD)儿童静息态下初级听觉脑区与全脑的功能连接特征,并与其感觉行为进行关联性分析。方法 收集34例ASD男童和29例健康对照男童的功能磁共振(fMRI)数据,基于静息态功能连接(rs-fcMRI)分析方法,将初级听觉脑区BA41/42作为种子区域,计算该区域与全脑的功能连接水平,并比较两组差异。采用简易感觉(SSP)量表评估ASD儿童的感觉行为,应用Pearson相关分析探索ASD儿童大脑rs-fcMRI功能连接强度与感觉行为之间的关联。结果 与对照组相比,ASD儿童BA41/42与左侧后扣带回正连接减弱,与SSP量表中触觉敏感、味觉/嗅觉敏感以及量表总分呈显著负相关(r=-0.496、-0.420、-0.415,P＜0.05);BA41/42与左内侧和旁扣带回正连接增强,与SSP量表中触觉敏感、低反应/寻求刺激、听觉过滤以及量表总分呈显著负相关(r=-0.650、-0.499、-0.447、-0.541,P＜0.05),与SSP量表等级中触觉敏感、味觉/嗅觉敏感、运动敏感、低反应/寻求刺激、力量低下/虚弱以及总量表得分均呈显著正相关(r=0.423、0.527、0.467、0.471、0.470、0.642,P＜0.05);BA41/42与左侧补充运动区正连接增强。结论 静息态下ASD儿童初级听觉脑区与全脑的功能连通性异于健康儿童,并与其异常的感觉行为明显相关。     

Graph Theory-Based Electroencephalographic Connectivity and Its Association with Ketogenic Diet Effectiveness in Epileptic Children

Ketogenic diet therapies (KDTs) are widely used treatments for epilepsy, but the factors influencing their responsiveness remain unknown. This study aimed to explore the predictors or associated factors for KDTs effectiveness by evaluating the subtle changes in brain functional connectivity (FC) before and after KDTs. Segments of interictal sleep electroencephalography (EEG) were acquired before and after six months of KDTs. Analyses of FC were based on network-based statistics and graph theory, with a focus on different frequency bands. Seventeen responders and 14 non-responders were enrolled. After six months of KDTs, the responders exhibited a significant functional connectivity strength decrease compared with the non-responders; reductions in global efficiency, clustering coefficient, and nodal strength in the beta frequency band for a consecutive range of weighted proportional thresholds were observed in the responders. The alteration of betweenness centrality was significantly and positively correlated with seizure reduction rate in alpha, beta, and theta frequency bands in weighted adjacency matrices with densities of 90%. We conclude that KDTs tended to modify minor-to-moderate-intensity brain connections; the reduction of global connectivity and the increment of betweenness centrality after six months of KDTs were associated with better KD effectiveness.     

孤独症谱系障碍男童初级听觉脑区与全脑功能连通性及与感觉行为的关联研究

目的 研究孤独症谱系障碍(ASD)儿童静息态下初级听觉脑区与全脑的功能连接特征,并与其感觉行为进行关联性分析。方法 收集34例ASD男童和29例健康对照男童的功能磁共振(fMRI)数据,基于静息态功能连接(rs-fcMRI)分析方法,将初级听觉脑区BA41/42作为种子区域,计算该区域与全脑的功能连接水平,并比较两组差异。采用简易感觉(SSP)量表评估ASD儿童的感觉行为,应用Pearson相关分析探索ASD儿童大脑rs-fcMRI功能连接强度与感觉行为之间的关联。结果 与对照组相比,ASD儿童BA41/42与左侧后扣带回正连接减弱,与SSP量表中触觉敏感、味觉/嗅觉敏感以及量表总分呈显著负相关(r=-0.496、-0.420、-0.415,P＜0.05);BA41/42与左内侧和旁扣带回正连接增强,与SSP量表中触觉敏感、低反应/寻求刺激、听觉过滤以及量表总分呈显著负相关(r=-0.650、-0.499、-0.447、-0.541,P＜0.05),与SSP量表等级中触觉敏感、味觉/嗅觉敏感、运动敏感、低反应/寻求刺激、力量低下/虚弱以及总量表得分均呈显著正相关(r=0.423、0.527、0.467、0.471、0.470、0.642,P＜0.05);BA41/42与左侧补充运动区正连接增强。结论 静息态下ASD儿童初级听觉脑区与全脑的功能连通性异于健康儿童,并与其异常的感觉行为明显相关。     

Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations

An automated sleep staging based on analyzing long-range time correlations in EEG is proposed. These correlations, indicating time-scale invariant property or self-similarity at different time scales, are known to be salient dynamical characteristics of stage succession for a sleeping brain even when the subject suffers a destructive disorder such as Obstructive Sleep Apnea (OSA). The goal is to extract a set of complementary features from cerebral sources mapped onto the scalp electrodes or from a number of denoised EEG channels. For this purpose, source localization/extraction and noise reduction approaches based on Independent Component Analysis were used prior to correlation analysis. Feature extracted segments were then classified in one of the five classes including WAKE, STAGE1, STAGE2, SWS and REM via an ensemble neuro-fuzzy classifier. Some techniques were employed to improve the classifier’s performance including Scaled Conjugate Gradient Method to speed up learning the ANFIS classifiers, a pruning algorithm to eliminate irrelevant fuzzy rules and the 10-fold cross-validation technique to train and test the system more efficiently. The performance of classification for two strategies including (1) feature extraction from effective cerebral sources and (2) feature extraction from selected channels of denoised EEG signals was compared and contrasted in terms of training errors and test accuracies. The first and second strategies achieved 92.23 and 88.74% agreement with human expert respectively which indicates the effectiveness of the staging system based on cerebral sources of activity. Our results further indicate that the misclassification rates were almost below 10%. The proposed automated sleep staging system is reliable due to the fact that it is based on the underlying dynamics of sleep staging which is less likely to be affected by sleep fragmentations occurred repeatedly in OSA.