优秀气手枪射击运动员临赛前脑电图变化的特征研究

目的:探讨我国优秀气手枪射击运动员赛前一天和临赛前脑电图变化的特征.方法:应用便携式无需屏蔽的脑电图测试系统,对参加好运北京气手枪射击比赛的我国优秀运动员的赛前一天和临赛前脑电图进行测试分析,比较安静状态和表象状态的变化.结果:赛前一天表象和安静状态相比,整个脑区α波能量百分比降低,β波能量百分比上升;临赛前比较该变化的趋势不明显;赛前一天的平均唤醒水平为17.91%±12.67%,临赛前平均为12.99%±11.65%(P＜0.01);赛前一天的疲劳参数表象状态较安静状态低(P＜0.01),应激参数表象状态高于安静状态(P＜0.05);临赛前的疲劳参数的表象和安静状态相比无差异(P＞0.05),应激参数表象状态小于安静状态(P＜0.01).结论:优秀气手枪射击运动员临赛前大脑平均唤醒水平低于赛前一天,大脑左右半球的协同能力提高,脑电图能在一定程度上反映赛前运动员的紧张度和疲劳度.     

Creating sense of presence in a virtual reality experience: Impact on neurophysiological arousal and attitude towards a winter sport

The authors of this study examined the impact of sense of presence on affective responses to a virtual reality experience and attitude towards a winter sport—luge. An experiment was implemented to compare virtual reality experience environments where a sense of presence was stimulated to the experimental group. Electroencephalogram was used to measure emotional arousal and self-report measures were used to assess attitudes. The experimental group with higher sense of presence showed higher arousal and attitude towards luge. While most of the literature of virtual reality has focused on creating a high-fidelity virtual environment, results of this study highlight the importance of creating an immersive environment in the physical space where a person experiences virtual reality. The authors further discuss the implications to scholars and managers.     

脑电非线性动力学分析在优秀射箭运动员中枢疲劳研究中的应用

研究目的:对12名备战北京奥运会的国家射箭队运动员脑电信号的最大李雅普诺夫指数、近似熵与复杂度等3个非线性特征参数进行分析,为研究大脑的高级功能提供了一条新的途径.结果表明:国家射箭队运动员的脑电最大李雅普诺夫指数、复杂度和近似熵等3个非线性特征参数不存在显著男、女性别差异;L-Z复杂度可敏感地反映出运动员是否处于中枢疲劳状态,近似熵可较敏感地反映出国家射箭队优秀运动员在不同的两个阶段中训练负荷的变化.虽然被试运动员的数量有限,但脑电最大李雅普诺夫指数、近似熵和复杂度等3个非线性特征参数与中枢神经疲劳程度之间存在着一定相关关系,对于分析和监测射箭运动员中枢神经系统疲劳程度是一种非常有效的方法,可作为反映其中枢神经系统机能的定性指标.     

癫痫脑电信号的非线性分析方法

癫痫是大脑神经元过度放电而导致的一种大脑功能障碍性疾病,癫痫发作对大脑的损害是不可逆的.目前,针对癫痫常用的几种非线性动力学分析方法包括相关维数,Lyapunov指数,复杂度等.这些方法可有效分析癫痫患者的脑电信号,对治疗癫痫具有重要临床价值.     

大学生网络成瘾研究初探

网络成瘾(IAD)是指由重复使用网络所导致的一种慢性或周期性的着迷状态,并带来难以抗拒的再度使用欲望,对于上网所带来的快感会产生心理与生理上的依赖,同时还会产生想要增加使用时间的张力等现象。文章对网络成瘾的定义、特征、分类及现状,并对IAD的成因、病理心理机制、产生的影响及评估方法等进行了阐述,重点对如何利用脑电/事件诱发电位(EEG/ERP)对网络成瘾进行了研究和介绍。     

脑电放大器体表电位测量设计与实现

提出了一种采用脑电放大器实现BSPM的方案。方案采用两台64导联脑电放大器采集体表电位信号,通过零相位数字滤波器滤除干扰,最后结合导联布置位置和处理后的信号重构出BSPM。实验结果表明,提出的方案能够有效获取到与心脏电活动对应的BSPM。提出的方法在复杂心脏疾病诊断上具有巨大的潜力。     

博弈神经机制评述

标准博弈论的形成是一个不断发展的过程,目前已经形成一套相当完备的体系。尽管标准博弈论的发展就是不断放松一系列严格假定的过程,但它仍然存在一个重大缺陷——严格的理性人假定。个体是如何进行博弈的,博弈的神经机制是什么已经成为博弈论研究的重点。文章回顾了有关博弈神经机制方面的研究成果,并指出当前研究多数处于一种假设状态,因此,对博弈神经机制的研究还有待于进一步的探索。     

EMG AND EEG PATTERNS IN JUVENILE TABLE TENNIS PLAYERS DURING IMAGERY

Many strdies have been conducted on imagery rehearsal to test psychoneuromascular theory,in which only adults or experienced athletes were taken as subjects. What about children who engage in athetictraining? Eighteen table tennis players of 8-11 years of age participated in the present experiment. Results showed that there was no significant difference of integrated EMG (IEMG) between baseline and imagery status. EEG test showed a different changing pattern during imagery compared with adults :EEG power ratio of 2HZ- 7. 9HZ in left and right occiputs and motor areas decreased,EEG power ratio of 11. 1HZ- 27HZ in left and rightmotor areas also decreased,while EEG power ratio of 8HZ-11HZ in all of four places and 1.11HZ-27HZ in both occiputs did not change. The correlation coeffcients botween IEMG and EEG power ratio of 8-11HZ inright occiput, 11.1-27HZ in left occiput,and 11.1 - 27HZ in right occiput were - 0. 88, -0.71 and 0. 65 respectively. The possible reasons for these differences from adults were discussed in terms of children's characteristics and lateralization of brain.     

Alertness staging based on improved self-organizing map

In order to classify the alertness status, 19 channels of electroencephalogram（EEG） signals from 5 subjects were acquired during daytime nap. Ten different types of features（including time domain features, frequency domain features and nonlinear features） were extracted from EEG signals, and an improved self-organizing map（ISOM） neuron network was proposed, which successfully identify three different brain status of the subjects： awareness, drowsiness and sleep. Compared with traditional SOM, the experiment results show that the ISOM generates much better classification accuracy, reaching as high as 89.59%.