Benefits of combination of electroencephalography, short latency somatosensory evoked potentials, and transcranial Doppler techniques for confirming brain death

Objective: Optimization of combining electroencephalography (EEG), short latency somatosensory evoked potentials (SLSEP) and transcranial Doppler (TCD) techniques to diagnose brain death. Methods: One hundred and eleven patients (69 males, 42 females) from the major hospitals of Zhejiang Province were examined with portable EEG, SLSEP and TCD devices. Re-examinations occurred ≤12 h later. Results: The first examination revealed that the combination of SLSEP and EEG led to more sensitive diagnoses than the combination of SLSEP and TCD. Re-examination confirmed this and also revealed that the combination of TCD and EEG was the most sensitive. Conclusion: The results show that using multiple techniques to diagnose brain death is superior to using single method, and that the combination of SLSEP and EEG is better than other combinations.

癫痫EEG信号相空间重构参数的计算和分析

根据癫痫发作过程中,EEG信号表现出来的发作间期和发作期2种不同的状态,通过分析发现在该过程中大脑动力系统存在不同的动力学嵌入空间,存在不同的吸引子。还应用伪邻点法、互信息法和C-C方法进行了推导和仿真,对2种不同状态进行相空间重构,确定了癫痫病人不同状态EEG不同吸引子的参数,并在此基础上提出了若干新的见解。

基于EEG熵值的驾驶员脑力负荷水平识别方法

为了对驾驶员脑力负荷予以有效识别,基于脑电信号指标构建了一种驾驶员脑力负荷识别方法.对驾驶员脑电信号进行快速傅里叶变换（FFT）,选取θ（4~8 Hz）,α（8~13 Hz）,β（13~30 Hz）3个频段的频谱幅值分别进行熵处理,对所得到的熵值作为脑力负荷识别参数,并对识别参数进行Kruskal-Wallis检验,选取差异最为显著的10项参数作为脑力负荷特征指标,在此基础上结合BP模型构建了驾驶员脑力负荷识别模型.基于驾驶模拟器实验数据,模型识别正确率为87.8%~90.4%.结果表明,该模型对驾驶员脑力负荷识别具有较高准确性,可实现不同驾驶员脑力负荷的有效识别,为未来自动辅助驾驶系统构建及车载信息系统优化设计提供算法依据.

一种EEG信号盲分离和分类的神经网络方法

提出一种采用多神经网络处理脑电 (EEG)信号的方法。首先 ,对混有噪声的脑电信号给出一种盲分离的自适应神经算法。通过寻求采样时间序列线性组合的kurtosis系数的局部极值 ,得出该算法的模型和步骤。在盲分离的基础上 ,对分离出的估计信号进一步利用Kohonen网络进行分类。将该算法用于 30 0个EEG样本处理 ,并给出处理结果。

一种新型EEG视觉诱发电位闪光刺激器的设计

在关于视觉诱发脑电信号的采集中,需要用闪光灯以不同频率、适当亮度的灯光刺激被测试者的眼睛,针对这一需要,设计了一种新型的基于Atmega8单片机控制驱动LED发光的视觉诱发电位刺激器,分析了具体的设计方案选择、硬件电路的设计和软件的设计,并最终实现了符合医用标准的视觉诱发电位刺激器.

EEG与SEP对脓毒症相关性脑病患者脑损伤程度评价

目的探讨脑电图(electroencephalogram,EEG)和体感诱发电位(subcortical evoked potentials,SEP)在脓毒症相关性脑病(Sepsis related encephalopathy,SAE)患者脑损伤程度评价中的应用效果。方法选取2018年1月—2020年1月福建医科大学附属协和医院重症医学科收治的30例SAE患者作为研究组,将同期收治的30例无脑病脓毒症患者作为对照组,均入院后接受EEG与SEP监测,根据监测结果评价SAE患者脑损伤程度。结果通过检测,研究组患者的EEG评级为V级18例、IV级6例、III级4例、II级2例,对照组患者分别为II级9例、I级21例,组间差异有统计学意义(P<0.05);研究组SEP检测,异常22例、正常8例,对照组分别为4例、26例,差异有统计学意义(P<0.05)。结论EEG、SEP可对SAE患者的脑损伤严重程度进行有效监测和评价,为临床治疗干预提供依据。

基于GRNN的可穿戴式脑电仪EEG疲劳检测

针对单电极可穿戴式脑电仪的脑电波信号(EEG)的疲劳状态智能识别,进行了基于广义回归神经网络(GRNN)的疲劳状态检测的研究。首先,通过调查问卷调查用户主观疲劳量,结合疲劳检测手环实现EEG数据的疲劳等级标记以建立数据集;其次,采用数据清洗等方式实现数据预处理并提取数据的时域特征、频域特征;运用主元分析进行特征降维;然后,建立GRNN疲劳识别模型并计算识别准确率;同时以支持向量机(SVM)方法作为对比实验检验模型效果;最后,以建立好的GRNN模型进行疲劳检测。研究发现,GRNN模型下EEG疲劳状态识别准确率最高为88.1%,相比SVM模型更高,对于EEG的疲劳状态的检测具有更好的稳定性和区分度。

Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

Seizure detection using earth movers' distance and SVM in intracranial EEG

Seizure detection is extremely essential for long-term monitoring of epileptic patients. This paper investigates the detection of epileptic seizures in multi-channel long-term intracranial electroencephalogram （iEEG）. The algorithm conducts wavelet decomposition of iEEGs with five scales, and transforms the sum of the three frequency bands into histogram for computing the distance. The proposed method combines a novel feature called EMD-L1, which is an efficient algorithm of earth movers＇ distance （EMD）, with support vector machine （SVM） for binary classification between seizures and non-sei- zures. The EMD-LI used in this method is characterized by low time complexity and high processing speed by exploiting the L~ metric structure. The smoothing and collar technique are applied on the raw outputs of SVM classifier to obtain more ac- curate results. Several evaluation criteria are recommended to compare our algorithm with other conventional methods using the same dataset from the Freiburg EEG database. Experiment results show that the proposed method achieves a high sensi- tivity, specificity and low false detection rate, which are 95.73 %, 98.45 % and 0.33/h, respectively. This algorithm is char- acterized by its robustness and high accuracy with the possibility of performing real-time analysis of EEG data, and may serve as a seizure detection tool for monitoring long-term EEG.

Macrostate and Microstate of EEG Spatio-Temporal Nonlinear Dynamics in Zen Meditation

Macrostate and microstate characteristics of interregional nonlinear interdependence of brain dynamics are investigated for Zen-meditation and normal resting EEG. Evaluation of nonlinear interdependence based on nonlinear dynamic theory and phase space reconstruction is employed in the 30-channel electroencephalographic (EEG) signals to characterize the functioning interactions among different local neuronal networks. This paper presents a new scheme for exploring the microstate and macrostate of interregional brain neural network interactivity. Nonlinear interdependence quantified by similarity index is applied to the phase trajectory reconstructed from multi-channel EEG. The microstate similarity-index matrix (miSIM) is evaluated every 5 millisecond. The miSIMs are classified by K-means clustering. The cluster center corresponds to the macrostate SIM (maSIM) evaluated by conventional scheme. Zen-meditation EEG exhibits rather stationary and stronger interconnectivity among frontal midline regional neural oscillators, whereas resting EEG appears to drift away more often from the midline and extend to the inferior brain regions.

基于脑电EEG信号的分析分类方法

随着脑科学与生物医学工程研究的不断深入,脑电信号的分析方法发展迅速。脑电信号的分析分类处理主要包含脑电信号预处理、特征提取和分类识别3个阶段,而每个阶段具有各种不同的处理方法,通过对不同阶段的分类处理方法进行分析,侧重关注现代脑电信号的预处理、特征提取和分类识别的重要内容及处理方法。

基于EEG的脑机接口发展综述

随着无线传输、机器学习、人工智能等技术的进步,基于脑电图(EEG)的脑机接口(BCI)技术的研究相应增加,作为一种变革性的通讯和控制技术,脑机接口可以广泛地应用于康复医疗、游戏娱乐、军事应用、家居智能等领域,具备千亿级别的应用市场;综述了基于EEG的典型脑机接口范式,包括MI-BCI、P300-BCI、SSVEP-BCI等范式的基本原理、研究现状和典型应用场景,对各类范式的优缺点进行了评价,提出了当前研究中面临的技术和伦理等方面的风险挑战,并对其发展和应用前景作了展望。

Deficits in Magnocellular Pathway in Developmental Dyslexia: A Functional Magnetic Resonance Imaging-Electroencephalography Study

Background: Magnocellular deficit theory is among the different hypotheses that have been proposed to explain the pathophysiology of developmental dyslexia (DD). Dysfunction of the magnocellular system in DD has been investigated using mainly visual evoked potentials (VEPs), particularly transient VEPs, although recently abnormal steady-state VEPs have also been reported. The brain regions responsible for the abnormal VEPs in DD have yet to be elucidated, however. In this study, we performed functional magnetic resonance imaging and electroencephalography (fMRI-EEG) simultaneously to elucidate the brain areas that were found in a previous study to be activated through stimulation of the magnocellular system, and then investigated the mechanism involved in the dysfunction seen in DD.Methods: Subjects were 20 healthy individuals (TYP group;13 men, 7 women;mean ± standard deviation age, 26.3 ± 5.53 years) and 2 men with DD (aged 42 and 30 years). Images of brain activity were acquired with 3-Tesla MRI while the viewing the reversal of low-spatial frequency and low-contrast black-and-white sinusoidal gratings. EEG was recorded concurrently to obtain steady-state VEPs.Results: Stimulus frequency-dependent VEPs were observed in the posterior region of the brain in the TYP group;however, VEP amplitudes in both DD patients were clearly smaller than those in TYP. fMRI images revealed that both the primary and secondary visual cortices were activated by black-and- white sinusoidal gratings in the TYP group, whereas activity in the visual cortex overall was reduced in both DD patients.Conclusions: Present low spatial and high reversal frequency visual stimuli activated the primary visual cortex presumably through predominant activation of the magnocellular pathway. This finding indicates that some cases of adult patients of DD involve impairment of the visual magnocellular system.

Cross-task emotion recognition using EEG measures: first step towards practical application

Electroencephalographic(EEG)-based emotion recognition has received increasing attention in the field of human-computer interaction(HCI)recently,there however remains a number of challenges in building a generalized emotion recognition model,one of which includes the difficulty of an EEG-based emotion classifier trained on a specific task to handle other tasks.Lit-tle attention has been paid to this issue.The current study is to determine the feasibility of coping with this challenge using feature selection.12 healthy volunteers were emotionally elicited when conducting picture induced and videoinduced tasks.Firstly,support vector machine(SVM)classifier was examined under within-task conditions(trained and tested on the same task)and cross-task conditions(trained on one task and tested on another task)for pictureinduced and videoinduced tasks.The within-task classification performed fairly well(classification accuracy:51.6%for picture task and 94.4%for video task).Cross-task classification,however,deteriorated to low levels(around 44%).Trained and tested with the most robust feature subset selected by SVM-recursive feature elimination(RFE),the performance of cross-task classifier was significantly improved to above 68%.These results suggest that cross-task emotion recognition is feasible with proper methods and bring EEG-based emotion recognition models closer to being able to discriminate emotion states for any tasks.

Comparison of Spatio-Spectral Properties of Zen-Meditation and Resting EEG Based on Unsupervised Learning

This paper reports distinct spatio-spectral properties of Zen-meditation EEG (electroencephalograph), compared with resting EEG, by implementing unsupervised machine learning scheme in clustering the brain mappings of centroid frequency (BMFc). Zen practitioners simultaneously concentrate on the third ventricle, hypothalamus and corpora quadrigemina touniversalize all brain neurons to construct a detached brain and gradually change the normal brain traits, leading to the process of brain-neuroplasticity. During such tri-aperture concentration, EEG exhibits prominent diffuse high-frequency oscillations. Unsupervised self-organizing map (SOM), clusters the dataset of quantitative EEG by matching the input feature vector Fc and the output cluster center through the SOM network weights. Input dataset contains brain mappings of 30 centroid frequencies extracted from CWT (continuous wavelet transform) coefficients. According to SOM clustering results, resting EEG is dominated by global low-frequency (<14 Hz) activities, except channels T7, F7 and TP7 (>14.4 Hz);whereas Zen-meditation EEG exhibits globally high-frequency (>16 Hz) activities throughout the entire record. Beta waves with a wide range of frequencies are often associated with active concentration. Nonetheless, clinic report discloses that benzodiazepines, medication treatment for anxiety, insomnia and panic attacks to relieve mind/body stress, often induce beta buzz. We may hypothesize that Zen-meditation practitioners attain the unique state of mindfulness concentration under optimal body-mind relaxation.

Online prediction of EEG based on KRLST algorithm

Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal processing.But the distribution of classic data sets seems relatively regular and simple in time series.The distribution of the electroencephalograph(EEG)signal is more randomness and non-stationarity,so online prediction of EEG signal can further verify the robustness and applicability of kernel adaptive algorithms.What’s more,the purpose of modeling and analyzing the time series of EEG signals is to discover and extract valuable information,and to reveal the internal relations of EEG signals.The time series prediction of EEG plays an important role in EEG time series analysis.In this paper,kernel RLS tracker(KRLST)is presented to online predict the EEG signals of motor imagery and compared with other 13 kernel adaptive algorithms.The experimental results show that KRLST algorithm has the best effect on the brain computer interface(BCI)dataset.

Epileptic Encephalopathies in Infants and Children: Study of Clinico-Electroencephalographic Spectrum in a Tertiary Hospital in Bangladesh

Background: The epileptic encephalopathies collectively exact an immense personal, medical, and financial toll on the affected children, their families, and the healthcare system. Objective: This study was aimed to delineate the clinical spectrum of patients with Epileptic encephalopathies (EEs) and classify them under various epileptic syndromes. Methods: This was a cross-sectional study that was carried out in the department of Neurophysiology of the National Institute of Neurosciences and Hospital, Bangladesh from July 2016 to June 2019. Children with recurrent seizures which were difficult to control and associated with developmental arrest or regression in absence of a progressive brain pathology were considered to be suffering from EE. Children under 12 years of age fulfilling the inclusion criteria were enrolled in the study. These patients were evaluated clinically and Electroencephalography (EEG) was done in all children at presentation. Based on the clinical profile and EEG findings the patients were categorized under various epileptic syndromes according to International League Against Epilepsy (ILAE) classification 2010. Results: A total of 1256 children under 12 years of age were referred to the Neurophysiology Department. Among them, 162 (12.90%) fulfilled the inclusion criteria. Most of the patients were male (64.2%) and below 1 year (37.7%) of age. The majority (56.8%) were delivered at the hospital and 40.1% had a history of perinatal asphyxia. Development was age-appropriate before the onset of a seizure in 38.9% of cases. Most (53.7%) of the patients had seizure onset within 3 months of age. Categorization of Epileptic syndromes found that majority had West Syndrome (WS) (37.65%) followed by Lennox-Gastaut syndrome (LGS) (22.22%), Otahara syndrome (11.73%), Continuous spike-and-wave during sleep (CSWS) (5.66%), Myoclonic astatic epilepsy (MAE) (4.94%), Early myoclonic encephalopathy (EME) (3.7%), Dravet syndrome (3.7%) and Landau-Kleffner syndrome (LKS) (1.23%). 9.26% of syndromes were unclassified. Conclusion: EEG was found to be a useful tool in the evaluation of Epileptic encephalopathies. The clinico-electroencephalographic features are age-related. Their recognition and appropriate management are critical.

巅峰式神经反馈训练提升射击表现效果和无应答者特性分析

为探索神经反馈训练在提升射击表现方面的应用效果和训练过程中的无应答者特性,开展一项用于提升射击表现的神经反馈训练(neurofeedback training for sport performance,SP-NFT)实验研究,招募20名受试者,进行2周4次的“巅峰”范式SP-NFT,采集受试者前、后测隐显目标射击表现和相关脑电(electroencephalograph,EEG)数据,检验SP-NFT对射击表现的提升效果、静息态EEG特征、SP-NFT期间正常组和无应答组EEG特性变化情况。结果表明:受试者后测射击成绩显著高于前测(P<0.01),静息态theta频带功率显著降低(P<0.01);相对正常受试者,无应答者在SP-NFT期间的努力程度更高,theta频段功率和SMR功率的变化程度更低,SP-NFT能够有效提升受试者射击表现,进一步揭示了无应答者的相关生理机制。研究成果为用于提升射击表现的SP-NFT技术进一步发展提供理论支撑和实验证据。

基于脑电微状态特征的正常老年人大脑网络特性研究

采用脑电(EEG)微状态方法,分析了94名受试者静息闭眼61通道脑电数据。基于脑电微状态时间序列、微状态转移概率提出微状态变化率与微状态转移熵计算方法,用于评估大脑功能网络动态信息交流特性及其复杂程度。结果显示,2组均得到A、B、C、D这4种经典微状态,相较于青年人,老年人微状态A、B特征及两者之间的转移概率均增加,而微状态C、D特征和微状态变化率以及微状态转移熵均降低。利用线性回归分析发现,脑电微状态特征与大脑内不同节律波能量相关,预示了正常老年人大脑动态特性发生改变,脑网络动态信息交流减弱,可能与老年人大脑内高频信号增加有关。