视听诱发脑电特征提取与大脑认知机理分析

为探索视听跨感觉大脑认知机理,基于Stroop效应设计视听觉刺激实验范式,利用Neuroscan40导联脑事件相关电位仪,连续动态采集视听诱发脑电信号,采用独立成分分析方法去除眼电伪迹,AR模型结合相干平均方法提取诱发脑电P300特征．通过对P300电位幅值和潜伏期的分析,研究视听觉诱发大脑认知的信息整合规律、交叉干扰作用和注意竞争效应．实验结果表明,大脑在视听双通道刺激下,更容易整合信息,具有视觉为主、听觉为辅的协同补偿作用,且视觉对听觉有较强的交叉干扰作用以及视觉为主导的竞争效应．该研究成果可以应用于神经信息处理、脑认知科学和脑一机交互系统中．     

经穴磁刺激线圈优化设计与仿真

经穴磁刺激与传统针灸、电刺激相比具有无创伤、无不适感、易重复和进行深部刺激等独特优点.磁刺激系统中载流线圈设计对磁场强度和磁场分布具有重要作用.在理论分析载流线圈磁场分布基础上,设计了不同8字形和碗状结构磁刺激线圈,并采用Ansys有限元分析软件对线圈沿径向和轴向产生的磁场分布进行仿真,研究8字形和碗状结构线圈的磁场聚焦性和刺激深度.结果表明,碗状线圈具有较好的磁场聚焦性且有一定的刺激深度,在皮层下1.5～2 cm左右仍有3～10 mT的磁场强度.同时获得了适合于经穴磁刺激系统的最佳线圈形状和参数,为穴位磁刺激技术应用于临床治疗奠定了良好基础.     

基于少次相干平均和样本熵的视听诱发脑电特征提取

针对脑电信号的非平稳性和非线性,采用少次相干平均结合样本熵的方法对视听诱发脑电信号进行特征提取.首先,对预处理后的脑电信号进行15次相干平均,获得视觉、听觉及视听觉诱发脑电的时域特征信号;然后,将该特征信号做为原始信号输入,构成m维矢量序列,计算相关导联在靶刺激、非靶刺激和自发脑电状态的样本熵值;最后,比较分析单一视觉、听觉和视听刺激下,不同状态脑电样本熵值,文中阐明了视听觉诱发下,大脑认知的复杂性和信息耦合性.结果显示:只进行少次相干平均即可有效提取视听刺激模式下脑电的样本熵特征量,减少了因长时间视觉刺激引起神经疲劳导致的误差.同时,靶刺激的出现可使脑电样本熵值增大,表明中枢神经系统与外周刺激发生信息耦合,导致了大脑系统复杂性的提高.该研究可以应用于神经认知科学和脑-机交互系统中.     

注意力对视觉刺激响应的调制作用

注意是事件相关电位(event-related potential,ERP)产生的基础,通常ERP产生于有较强随机性的自发脑电(electroencephalogram,EEG)背景中,自发脑电干扰成为提取高质量ERP信号的主要障碍.本文采用稳态刺激诱发稳态视觉诱发电位(steady state visual evoked potential,SSVEP),得到特定频率和相位调制的背景脑电,并实施不同对比度的选择性注意视觉刺激,通过比较分析所诱发产生的ERP信号波形及其P1与N1成分幅值和潜伏期变化,研究不同背景脑电及刺激强度条件下注意力对视觉刺激诱发响应的调制作用.结果表明,在特定背景脑电节律振荡与初始相位及视觉刺激对比度时,注意力对视觉刺激响应有显著调节作用,主要体现在视觉注意条件下会增加ERP中N1成分幅值并具有显著性差异,注意力对P1和N1成分的潜伏期皆无显著性差异影响.研究结果提示在设计ERP诱发实验范式时可利用注意力显著调节作用赋予背景脑电预定节律和初始相位特征,以便在ERP提取和识别时消除自发脑电干扰,获得理想研究结果.     

基于体感电刺激的脑-机接口实验范式初探（英文）

大脑对各类感觉输入(视、听模态)会产生不同的响应信号,脑-机接口正是利用这一响应信号实现大脑与外部设备间直接的通讯.然而以电刺激作为脑-机接口的输入模态还未有报道.本研究尝试通过使用体感电刺激作为脑-机接口的输入,从而诱发事件相关电位(ERP).在整个实验中,分别使用视觉、听觉以及电刺激作为诱发因素,针对每种条件下的事件相关电位及其分类准确率开展对比分析.结果显示电刺激所诱发的事件相关电位幅值较高且具有相对稳定的潜伏期,其分类准确率高于听觉刺激范式.也就表明了以不同刺激强度作为参数的电刺激范式作为脑-机接口应用的可行性,这将进一步拓展脑-机接口的应用领域.     

脑电-肌电信号样本熵与小波熵分析

为了探索自主动作和电刺激产生动作两种不同模式下的大脑运动皮层活动与肌肉收缩之间的关系,搭建了脑电和肌电实验测量平台,设计了自主腕部外旋和穴位电刺激两种实验动作模式,同步采集不同动作模式诱发的脑电信号和表面肌电信号,计算和分析信号的样本熵与小波熵.结果发现穴位电刺激模式下,脑电信号样本熵增大,肌电信号样本熵减小,脑-肌电的平均互样本熵增大,肌电信号的小波熵明显减小.表明穴位电刺激使大脑活动复杂性提高,肌肉活动的有序性增强,出现了优势节律,脑-肌电协同性提高.     

基于脑电非线性动力学特征探究TEAS内关穴对焦虑的影响

基于脑电非线性动力学特征,探究经皮穴位电刺激(TEAS)内关穴对焦虑的影响及其机制.实验选取l2位焦虑受试者随机分为TEAS内关穴干预组(穴位组)和TEAS非穴位干预组(非穴位组),分别采集电刺激前后各组受试者脑电数据,提取脑电信号的特征近似熵(ApEn)与关联维数(D2),分析大脑复杂度的变化,同时比较电刺激前后焦虑...     

经穴电刺激诱发的脑-肌电信号相干性研究

为探索自主动作和穴位电刺激诱发的大脑皮层活动与肌肉收缩之间的功能耦合作用,设计穴位电刺激和自主动作两种实验模式,分别采集对应的脑电信号和表面肌电信号,对信号进行预处理得到纯净的诱发脑电和肌电信号.然后进行小波变换,获得信号的小波谱和小波交叉谱,计算小波相干系数,分析脑电和肌电信号的时-频相干特性.结果表明:穴位电刺激诱发的脑-肌电相干性主要集中在15~25 Hz信号频段,对应于大脑β节律;而自主动作下的脑-肌电相干性集中在30~36Hz频段,对应于大脑γ节律;不同导联的脑-肌电信号相干特性,表明穴位电刺激诱发的腕部肌肉动作与对侧大脑运动区相干性最强.该研究为运动功能康复和智能假肢开发奠定了基础.     

基于脑电和磁刺激的脑功能网络研究

大脑的结构和功能具有高度复杂性,其功能执行需要依赖于脑功能区之间的相互作用所构成的网络实现.脑电(EEG)作为一种无创医学检测技术因包含了大量的生理、病理信息且能够反映大脑的功能活动状态,被广泛应用于脑科学以及认知神经科学的研究中.经颅磁刺激(TMS)技术作为一种新型的刺激方式,因具有众多显著优势被应用于临床研究并在人体神经功能调控、疾病治疗以及康复理疗等方面表现出很好的应用前景.鉴于此,本文首先分析了脑网络研究的必要性及迫切性,回顾了近年来国内外基于EEG、功能磁共振成像(f MRI)等技术的脑功能网络研究成果,重点介绍了基于EEG和TMS技术的脑功能网络的构建与分析,最后对脑网络研究中存在的问题及该领域面临的挑战进行了讨论.     

Chirp信号发生器的设计与实现

本文利用DDS技术在FPGA平台上设计实现了一种Chirp信号发生器,该信号发生器能产生标准的Chirp波形和正余弦波形,通过PC机软件可调整波形的类型、频率和相位。该信号发生器输出频率范围为0．01Hz-25MHz,频率分辨率为0．01Hz,具有控制灵活,输出稳定的优点。     

Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

Electroencephalogram (EEG) is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain, measured and recorded chronologically the surface of the scalp from the brain. The recorded signals from the brain are rich with useful information. The inference of this useful information is a challenging task. This paper aims to process the EEG signals for the recognition of human emotions specifically happiness, anger, fear, sadness, and surprise in response to audiovisual stimuli. The EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp, in response to audiovisual stimuli for the mentioned emotions. Using a bandpass filter with a bandwidth of 1–100 Hz, recorded raw EEG signals are preprocessed. The preprocessed signals then further analyzed and twelve selected features in different domains are extracted. The Random forest (RF) and multilayer perceptron (MLP) algorithms are then used for the classification of the emotions through extracted features. The proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80% and 88% using MLP and RF classifiers respectively on hybrid features for experimental signals of different subjects. The proposed model outperforms in terms of cost and accuracy.     

Performance modeling of AMR refrigerators

A system modeling approach for predicting the performance of active magnetic regenerators using a one-phase approximation is presented. The approach is described for an arbitrary AMR device independent of the magnetic refrigerant, thermal losses or magnetic waveform. A general expression for magnetic work is derived which can be used for cycles where the low-field intensity is not zero. Additionally, a means of treating the varying magnetic field waveform as a single high and low field is described. The model is applied to a permanent magnet magnetic refrigerator using water–glycol as the heat transfer fluid. Simulated results are compared to experimental data which vary by heat load, frequency and utilization. A sensitivity analysis is performed using utilization, adiabatic temperature change, effective conductivity and particle size as independent variables. Comparisons to experimental data show that reducing the calculated magnetocaloric effect by 25% provides good agreement between simulations and experimental results.     

超磁致伸缩棒磁场强度建模及线圈优化分析

超磁致伸缩棒上的磁场强度对超磁致伸缩致动器(GMA)至关重要,因其幅值和上升、下降时间直接影响致动器的输出力和响应时间.建立电压到磁场强度的模型,并提出较合理的线圈优化方案.将线圈充、放电过程简化为一阶RL线性电路的暂态过程,计算得到线圈电流,并根据线圈电流建立超磁致伸缩棒上的磁场强度模型.由模型可知,致动器尺寸有限制时,棒上磁场强度的优化应主要考虑线圈匝数;通过分析线圈匝数对磁场强度稳态值、上升时间和下降时间的影响确定匝数的取值范围.向线圈施加不同频率和幅值的方波电压信号,得到的模型曲线与测得的实验结果相吻合,从而验证了模型的正确性.     

The local mean decomposition and its application to EEG perception data

This paper describes the local mean decomposition (LMD), a new iterative approach to demodulating amplitude and frequency modulated signals. The new method decomposes such signals into a set of functions, each of which is the product of an envelope signal and a frequency modulated signal from which a time-varying instantaneous frequency can be derived. The LMD method can be used to analyse a wide variety of natural signals such as electrocardiograms, functional magnetic resonance imaging data, and earthquake data. The paper presents the results of applying LMD to a set of scalp electroencephalogram (EEG) visual perception data. The LMD instantaneous frequency and energy structure of the EEG is examined, and compared with results obtained using the spectrogram. The nature of visual perception is investigated by measuring the degree of EEG instantaneous phase concentration that occurs following stimulus onset over multiple trials. The analysis suggests that there is a statistically significant difference between the theta phase concentrations of the perception and no perception EEG data.     

Epileptic Seizures Diagnosis Using Amalgamated Extremely Focused EEG Signals and Brain MRI

There exists various neurological disorder based diseases like tumor, sleep disorder, headache, dementia and Epilepsy. Among these, epilepsy is the most common neurological illness in humans, comparable to stroke. Epilepsy is a severe chronic neurological illness that can be discovered through analysis of the signals generated by brain neurons and brain Magnetic resonance imaging (MRI). Neurons are intricately coupled in order to communicate and generate signals from human organs. Due to the complex nature of electroencephalogram (EEG) signals and MRI’s the epileptic seizures detection and brain related problems diagnosis becomes a challenging task. Computer based techniques and machine learning models are continuously giving their contributions to diagnose all such diseases in a better way than the normal process of diagnosis. Their performance may sometime degrade due to missing information, selection of poor classification model and unavailability of quality data that are used to train the models for better prediction. This research work is an attempt to epileptic seizures detection by using a multi focus dataset based on EEG signals and brain MRI. The key steps of this work are: feature extraction having two different streams i.e., EEG using wavelet transformation along with SVD-Entropy, and MRI using convolutional neural network (CNN), after extracting features from both streams, feature fusion is applied to generate feature vector used by support vector machine (SVM) to diagnose the epileptic seizures. From the experimental evaluation and results comparison with the current state-of-the-art techniques, it has been concluded that the performance of the proposed scheme is better than the existing models.

     

含裂纹故障的齿轮系统动力学特性研究及其故障特征分析

考虑齿轮的时变啮合刚度、传动误差和轴承支撑刚度的影响,建立含齿根裂纹故障的齿轮系统多自由度力学模型,基于动力学方法对其故障机理进行研究。通过材料力学的方法计算齿轮在正常和含裂纹两种情况下的啮合刚度,对比两种刚度曲线的变化趋势,便于进行精确的动力学特性分析;对建立的模型求解系统的动态响应,结果表明当齿根存在裂纹时,其时域波形中会出现周期性的冲击现象,频谱中在啮合频率的基频及其倍频等地方形成一系列等间隔的边频谱线,其间隔大小等于故障齿轮的转频;这些边频成分幅值较低,能量分散且分布不均匀,在不同频带的幅值大小存在差异。针对上述特点,通过正交小波包方法对信号的频带进行分解,应用倒频谱分析各子频带信号的边频成分;结果表明,该方法能够有效的提高信号的信噪比,有助于识别和提取信号中由裂纹故障引起的边频成分。     

Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection

The electroencephalogram (EEG) is the frequently used signal to detect epileptic seizures in the brain. For a successful epilepsy surgery, it is very essential to localize epileptogenic area in the brain. The signals from the epileptogenic area are focal signals and signals from other area of the brain region nonfocal signals. Hence, the classification of focal and nonfocal signals is important for locating the epileptogenic area for epilepsy surgery. In this article, we present a computer aided automatic detection and classification method for focal and nonfocal EEG signal. The EEG signal is decomposed by Dual Tree Complex Wavelet Transform (DT‐CWT) and the features are computed from the decomposed coefficients. These features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier. The proposed system achieves 98% sensitivity, 100% specificity, and 99% accuracy for EEG signal classification. The experimental results are presented to show the effectiveness of the proposed classification method to classify the focal and nonfocal EEG signals. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 277–283, 2016     

Three‐dimensional FDTD modeling of neurons to solve EEG and MEG forward problem

Neuronal activities including calcium sodium current, ligands current, and synaptic transmembrane current create electromagnetic fields. Here, an analytic method is suggested to obtain the electromagnetic fields and potential signals resulting from the function of nerve cells inside the brain. Modeling simulates the behavior of cells three‐dimensionally. The proposed method employs the electric scalar potential and magnetic vector potential to solve the time‐domain three‐dimensional equations using the partial differential method. All ion flows are considered as electrical current densities. In this method, the brain and desired cells are meshed to solve the problem using the numerical method. As an example, the electric fields, magnetic fields, and signals generated by cingulum nerve fibers are illustrated and compared in C_z, F_z, and T3 electrode positions. A direct analysis method based on the same mechanism and biophysics of the nervous system is proposed. Employing this direct method leads not only to a better understanding of neuronal activity but also to a more accurate vision regarding the accuracy/inaccuracy of experimental and inverse methods. The analysis of these data provides insights into the brain function processes.     

Improving data acquisition for fast-scan cyclic voltammetry.

Described is an improved data acquisition system for fast-scan cyclic voltammetry (FSCV). The system was designed to significantly diminish noise sources that were identified in previously recorded FSCV measurements for the detection of neurotransmitters. Minimized noise is necessary to observe the low concentrations of neurotransmitters that are physiologically important. The system was based on a high-speed, 16-bit AD/DA acquisition board that allowed high scan rates and better resolved the small faradaic currents which remained after background subtraction. Irregularities that occur when independent timing sources are used for generation of the voltage waveform and collection of the current can create large noise artifacts near the voltage limits during FSCV. These were eliminated by the use of a single acquisition board that generated the voltage waveform and collected the current. Noise from frequency drift of the power line was eliminated through the use of a phase-locked loop. To demonstrate the improved performance of the system, data were collected using carbon-fiber microelectrodes in a flow injection analysis system and in brain slices. This new data acquisition system performed significantly better than another system previously used in our laboratory without these features. The improved detection limits of the new system allowed clearly resolved current spikes featuring pre-release "feet" to be recorded adjacent to individual mast cells following chemical stimulation. When combined with false-color plots, the low-noise system facilitated identification of dopamine release in a freely moving animal.