基于嵌入式系统的诱发脑电信号采集仪器设计

在建立了一个脑电信号源模型的基础上,仔细分析了脑电信号放大器的干扰与噪声,提出了抑制干扰与噪声的一系列措施．然后根据脑电信号的特点着重讨论了前置放大器的设计,介绍了整个脑电信号采集系统的组成,以及软件设计方面的任务分析、数字滤波器和关键的数据结构,最后讲解了诱发事件与诱发脑电信号的同步关系和数据的封装．     

BCI-代数中两元素之积的属性

研究了当一个BCI-代数中的两元素分别属于它的BCK-部分、结合部分、广义结合部分、拟结合部分及它们的余部时,两元素之积的属性.     

亚BCI-代数与BCI-代数

讨论文〔1〕中引入的亚BCI—代数与BCI—代数的关系,研究亚BCI—代数成为BCI—代数的条件,特别是亚BCI—代数成为P—半单BCI—代数的条件.     

BCI-代数的伴随元与BCI-代数的根性

给出了BCI-代数的伴随元与ZTR型BCI-代数的概念,证明了一个ZTR型BCI-代数是一个P-半单BCI-代数。     

Individualization of data-segment-related parameters for improvement of EEG signal classification in brain-computer interface

In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.     

链接分析工具——博客链接索引(BSI)的功能与应用

针对自形开发的链接分析工具——博客链接索引（BSI）的功能与应用,描述开发BSI的背景、理论基础、数据库结构设计等;以演示的方式介绍BSI的查询与统计两大功能;分析BSI的应用范围,包括网络计量学、数据挖掘、搜索引擎等方面。     

波罗的海好望角型船运价指数波动分析

为提高国际干散货航运市场预测的可靠性,通过对国际干散货航运市场中波罗的海好望角型船市场运价指数(Baltic Capesize Index,BCI)序列以及对数收益率序列的平稳性、异方差性进行分析和检验,验证了BCI半月对数序列存在单位根,是非平稳序列,服从随机游走的假设.对BCI半月对数收益率序列进行单位根检验的结果表明,BCI半月对数收益率序列是平稳的.通过使用GARCH(1,1)模型分析发现,BCI半月对数收益率序列有明显的波动集聚效应.结论可用于国际干散货航运市场预测方法的改善.     

能量熵在运动想象脑电信号分类中的应用

对脑电信号进行特征提取和分类是脑机接口研究的核心问题,通过利用不同运动想象脑电信号能量熵的变化,从能量熵中提取特征,利用自定义基于统计理论分类方法进行分类,结果均达到90%以上。     

关于弱FI-代数和弱MV-代数的一点注记

证明了弱FI－代数与BCI－代数是互相等价的。因而，可以充分利用BCI－代数的理论来研究弱FI－代数，此外，还证明弱MV－代数与MV－代数本质上也是同一概念。     

Tiny noise,big mistakes: adversarial perturbations induce errors in brain–computer interface spellers

An electroencephalogram (EEG)-based brain–computer interface (BCI) speller allows a user to input text to a computer by thought. It is particularly useful to severely disabled individuals, e.g. amyotrophic lateral sclerosis patients, who have no other effective means of communication with another person or a computer. Most studies so far focused on making EEG-based BCI spellers faster and more reliable; however, few have considered their security. This study, for the first time, shows that P300 and steady-state visual evoked potential BCI spellers are very vulnerable, i.e. they can be severely attacked by adversarial perturbations, which are too tiny to be noticed when added to EEG signals, but can mislead the spellers to spell anything the attacker wants. The consequence could range from merely user frustration to severe misdiagnosis in clinical applications. We hope our research can attract more attention to the security of EEG-based BCI spellers, and more broadly, EEG-based BCIs, which has received little attention before.