平面和立体图像的ERP特性研究

基于ERP技术,比较了一系列的平面和立体图片刺激的脑电信号。通过分析比较平面和立体图片的脑电信号,发现立体图像诱发的P300成分波幅比平面图像的P300成分波幅大。在非注意条件下,与平面相比,立体图像诱发的N140成分潜伏期较长。注意刺激图片诱发总能量均比非注意刺激图片产生的能量大。     

融合共空间模式与脑网络特征的EEG抑郁识别

提出共空间模式算法和脑网络拓扑属性融合的脑电信号（electroencephalography,EEG）特征,结合深度学习模型时序卷积网络（temporal convolution network,TCN）对抑郁组和对照组进行分类。根据相位锁值构建电极通道间相位同步性功能网络,分析不同频段下两种类别的功能连接模式。采用多特征融合方法将共空间模式特征和脑网络拓扑特征结合起来,最后结合Fisher score特征选择方法和分类器依赖结构,得到低维高效的特征子集并应用TCN进行分类。在抑郁数据集上的实验结果验证了所提策略的有效性。     

Naltrexone alteration of the nicotine-induced EEG and mood activation response in tobacco-deprived cigarette smokers.

The neural mechanisms contributing to the arousal-eliciting actions of smoking and nicotine involve multiple neurotransmitter systems. The current study examined the role of opioid neurotransmission in modulating the neuroelectric- and mood-activating response to acute nicotine administration in overnight tobacco-deprived smokers. In a randomized, double-blind, placebo-controlled design involving 18 (10 male, 8 female) overnight tobacco-abstinent smokers, spectrally analyzed electroencephalographic (EEG) activity and subjective reports of mood, euphoria, and smoking withdrawal were assessed in response to nicotine gum (4 mg) after pretreatment with placebo or with 50 mg of the opioid antagonist naltrexone. In addition to reducing withdrawal symptoms and increasing euphoria ratings, as well as subjective alertness in male participants, nicotine induced an EEG arousal response consisting of diffuse slow wave (delta, theta) amplitude reductions, frontal fast alpha wave amplitude increments, and elevations in beta wave amplitude, which were greater in female than in male smokers. Naltrexone attenuated the alerting and euphoric actions of nicotine but did not affect nicotine's ameliorating effects on withdrawal symptoms. Nicotine-induced frontal reductions in delta and global reductions in theta were prevented by naltrexone pretreatment, as were increases in anterior recordings of relative fast alpha. These findings suggest that the opioid system is involved in nicotine-induced subjective and neuroelectric arousal and implicate opioid-cholinergic interactions in the elicitation of these arousal responses to nicotine. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neuroadaptive output feedback control for nonlinear nonnegative dynamical systems with actuator amplitude and integral constraints

A neuroadaptive output feedback control architecture for nonlinear nonnegative dynamical systems with input amplitude and integral constraints is developed. Specifically, the neuroadaptive controller guarantees that the control amplitude as well as the integral of the control input over a given time interval are constrained, and the physical system states remain in the nonnegative orthant of the state space. The proposed approach is used to control the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for noncardiac surgery in the face of infusion rate constraints and an integral drug dosing constraint over a specified time period. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive control for non‐negative and compartmental dynamical systems with applications to general anesthesia

Non‐negative and compartmental dynamical system models are composed of homogeneous interconnected subsystems or compartments which exchange variable non‐negative quantities of material with conservation laws describing transfer, accumulation, and elimination between the compartments and the environment. These models are widespread in biological and physiological sciences and play a key role in understanding these processes. In this paper, we develop a direct adaptive control framework for linear uncertain non‐negative and compartmental systems. The proposed framework is Lyapunov‐based and guarantees partial asymptotic set‐point regulation; that is, asymptotic set‐point stability with respect to part of the closed‐loop system states associated with the plant. In addition, the adaptive controller guarantees that the physical system states remain in the non‐negative orthant of the state space. Finally, a numerical example involving the infusion of the anesthetic drug propofol for maintaining a desired constant level of depth of anesthesia for non‐cardiac surgery is provided to demonstrate the efficacy of the proposed approach. Copyright © 2003 John Wiley & Sons, Ltd.     

Individual differences in mental activity at sleep onset.

Reports of sleep-onset and nocturnal (EEG Stage 1, REM period) mentation, CPIs, and thematic fantasy responses were collected from 32 young-adult Ss. In confirmation of recent findings by Foulkes and Vogel, dreamlike mental activity was found to occur with fairly substantial frequency at sleep onset, albeit with wide individual differences. Patterning of personality correlates with hypnagogic and nocturnal dream recall suggests that the former varies directly with waking ego strength and adaptive flexibility, while the latter varies directly with indications of waking maladaptive symptomatology. A psychodynamic interpretation of these results is offered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于皮层慢电位的自发脑电实验设计与研究

针对基于自发脑电信号的脑机接口研究,设计了一种科学的且易实现的运动想象实验范例,利用运动想象脑电作为BCI的控制信号.该实验方案能有效地获得可识别的、具有特征性的自发脑电,满足脑机接口实验要求,为BCI的研究提供了一种更加自然、更加实用的控制方式.     

EEG source localization using a sparsity prior based on Brodmann areas

Localizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive study of brain dynamics. Generally, the source localization process involves a high‐dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In this article, we propose a novel method for EEG source localization. The proposed method is based on dividing the cerebral cortex of the brain into a finite number of “functional zones” which correspond to unitary functional areas in the brain. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF), and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%.     

不同精神状态下EEG序列复杂性研究

构造了与脑电的源熵相同的(0,1)分布随机序列,以及用白噪激励依据脑电构造出的AR模型而得到的AR序列来作为伪脑电信号。通过比较这三种序列的复杂度,证明了脑电远非随机信号,而是存在某种模式,这种模式可以由AR模型部分表示出来。在此基础上,对三种精神状态下的脑电序列的复杂度进行了双因素方差分析,结果表明复杂度可以显著地区分这三种状态。     

Analysis of Collaborative Brain Computer Interface (BCI) Based Personalized GUI for Differently Abled

Brain-Computer Interfaces (BCI) use Electroencephalography (EEG) signals recorded from the brain scalp, which enable a communication between the human and the outside world. The present study helps the patients who are people locked-in to manage their needs such as accessing of web url’s, sending/receiving sms to/from mobile device, personalized music player, personalized movie player, wheelchair control and home appliances control. In the proposed system, the user needs are designed as a button in the form of a matrix, in which the main panel of rows and columns button is flashed in 3 sec intervals. Subjects were asked to choose the desired task/need from the main panel of the GUI by blinking their eyes twice. The double eye blink signals extracted by using the bio-sensor of NeuroSky’s mind wave device with portable EEG sensors are used as the command signal. Each task is designed and implemented using a Matlab tool. The developed Personalized GUI application collaborated with the EEG device accesses the user’s need. Once the system identifies the desired option through the input control signal, the appropriate algorithm is called and performed. The users can also locate the next required option within the matrix. Therefore, users can easily navigate through the GUI Model. A list of personalized music, movies, books and web URL’s are preloaded in the database. Hence, it could be suitable to assist disabled people to improve their quality of life. Analysis of variance (ANOVA) is also carried out to find out the significant signals influencing a user’s need in order to improve the motion characteristics of the brain computer interface based system.