Real-world applications for brain-computer interface technology

The mission of the Georgia State University BrainLab is to create and adapt methods of human-computer interaction that will allow brain-computer interface (BCI) technologies to effectively control real-world applications. Most of the existing BCI applications were designed largely for training and demonstration purposes. Our goal is to research ways of transitioning BCI control skills learned in training to real-world scenarios. Our research explores some of the problems and challenges of combining BCI outputs with human-computer interface paradigms in order to achieve optimal interaction. We utilize a variety of application domains to compare and validate BCI interactions, including communication, environmental control, neural prosthetics, and creative expression. The goal of this research is to improve quality of life for those with severe disabilities.     

BCI Meeting 2005--workshop on BCI signal processing: feature extraction and translation.

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.     

脑电 BCI 系统的软硬件开发平台发展现状

脑-机接口系统(brain-computer interface,BCI)是一种将大脑活动信息直接转换为人工输出的系统,允许用户通过思维 活动直接控制外部设备。 脑电图技术(electroencephalogram,EEG)可以实时获取大脑活动产生的神经生理电信息,具有无创、低 廉、高时间分辨率等优点,是 BCI 获取大脑活动信息的主流方式之一。 脑电 BCI 系统具有脑电信号采集、处理和输出结果的功 能,能够诱发特征脑电,并控制外部设备,在康复、医疗诊断和神经科学研究等领域具有巨大的应用价值。 随着脑电 BCI 系统应 用需求不断增加,确保其快速高效地部署和应用的技术越来越重要。 结合近些年脑电 BCI 系统研究和应用,综合论述目前用于 开发脑电采集和编解码的硬件和软件平台的技术,分析归纳其当前现状与未来趋势,以促进开发脑电 BCI 系统软硬件平台的有 效发展。     

Brain-computer interface technology: a review of the first international meeting.

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.     

基于广域响应的大电网暂态稳定判别原型系统

基于响应的暂态稳定判别与控制技术是当前电力系统安全稳定领域的研究热点。目前已经提出了多种不同原理的暂态稳定判别技术,但距工程应用尚需解决很多问题。为分析比较各种判别技术的有效性和适用性,开发了一个基于广域响应的大电网暂态稳定判别原型系统。原型系统以PMU实时量测数据作为输入,分别实现了多种基于响应的暂态失稳判别技术的实时评估与可视化展示。介绍了原型系统的框架结构设计、功能模块实现和工程应用情况,并从时间尺度、数据量测以及控制策略生成等方面为基于响应的暂态稳定控制的下一步发展方向提出了建议。工程应用表明所开发的原型系统具有较好的使用价值,是分析比较各种基于广域响应的暂态稳定判别技术的有效工具。     

A framework for UMTS air interface analysis

An integrated approach is presented for uplink and downlink air interface capacity evaluation and traffic analysis for the multiservice Universal Mobile Telecommunications System (UMTS). Within this framework the effects of both soft handoff (SHO) and fast power control (FPC) are considered. The canonical capacity of any given service, defined as the maximum number of concurrent users of that service, is derived; and the network total load is described as a function of this parameter and the number of concurrent users. The concept of virtual slots is defined within this integrated framework to obtain an accurate estimate for the blocking probability of each service type. A number of case studies are also presented to compare the results of utilizing the proposed framework with the results of network simulation. This comparison demonstrates the accuracy and usefulness of the proposed approach for network design and dimensioning.     

BCI meeting 2005--workshop on technology: hardware and software.

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users' needs and the need for consistent standards in BCI research.     

Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI

System calibration and user training are essential for operating motor imagery based brain-computer interface (BCI) systems. These steps are often unintuitive and tedious for the user, and do not necessarily lead to a satisfactory level of control. We present an Adaptive BCI framework that provides feedback after only minutes of autocalibration in a two-class BCI setup. During operation, the system recurrently reselects only one out of six predefined logarithmic bandpower features (10-13 and 16-24 Hz from Laplacian derivations over C3, Cz, and C4), specifically, the feature that exhibits maximum discriminability. The system then retrains a linear discriminant analysis classifier on all available data and updates the online paradigm with the new model. Every retraining step is preceded by an online outlier rejection. Operating the system requires no engineering knowledge other than connecting the user and starting the system. In a supporting study, ten out of twelve novice users reached a criterion level of above 70% accuracy in one to three sessions (10-80 min online time) of training, with a median accuracy of 80.2 ± 11.3% in the last session. We consider the presented system a positive first step towards fully autocalibrating motor imagery BCIs.     

The Wadsworth BCI Research and Development Program: at home with BCI.

The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.     

Somatotopy of the motor cortex after long-term spinal cord injuryor amputation

Certain brain-computer interface (BCI) methods use intrinsic signals from the motor cortex to control neuroprosthetic devices. The organization of the motor pathways in those populations likely to use neuroprosthetic devices, therefore, needs to be determined; there is evidence that following disease or injury the representation of the body in the motor cortex may change. In this study, functional MRI measures of somatotopy following spinal cord injury (SCI) showed evidence of changes in limb representations in the motor cortex. Subjects with chronic SCI had unusual cortical patterns of activity when attempting to move limbs below their injury; amputees showed a more normal somatotopy. The functional reorganization may affect optimal implanted electrode placements for invasive BCI methods for these different populations     

基于视听交互刺激的认知机理与脑机接口范式研究进展

与人类视听觉感知密切相关的图像、语音和文本(语言)信息在社会经济发展与国家安全保障等领域中扮演着重要角色,脑机接口(BCI)是无需外围神经肌肉便可操控外部设备、使"思想"直接变成"行动"的创新技术,基于视、听觉刺激的BCI系统具有极其广阔应用前景。目前认知心理学研究普遍认为视听交互刺激模式可产生比视或听单模态刺激更强的事件相关电位信号,因而可使识别大脑思维模式的正确率更高和响应速度更快,然而BCI研究至今尚难有充分验证其优势的成果。分别从认知心理机制和BCI实验范式两方面综述了视听交互刺激的信息整合机制、视听刺激之间的协同与竞争关系、匹配度对刺激效果的影响及相关BCI实验范式的研究现状;深入分析了视听交互刺激范式未在现有BCI系统中体现其优越性的原因;最后从BCI实验范式选择、系统硬件性能改进、脑电信号处理等方面提出了改进思路并展望了基于视听交互刺激BCI范式研究未来动向。     

Social informatics and service learning as teaching models

We have proposed a framework to integrate social and ethical issues into science and engineering curricula. We have also shown how such a framework might be implemented across a curriculum, and how it might be applied to the study of a specific scientific problem. Our framework is based on basic foundations of applied engineering ethics, adaptation of the structure of the meta-discipline of social informatics, and the use of a service-learning pedagogy based on Perry's (1981) cognitive-structural model for intellectual development in college students. The structure of the taxonomy of social informatics research and issues offer a well-established framework that can be re-contextualized for many science and engineering fields. Service learning offers a clear model for giving students the opportunity to practice ethical and social concepts imparted to them in the classroom.     

An integrated approach to power system reliability assessment

This paper presents an integrated approach to power system reliability assessment. It gives an updated view of the subject and aims to unify, classify and extend some fundamental ideas and controversial issues which provide theoretical and practical support to these studies. The resulting conceptual framework lays the basis for a useful taxonomy which may be recalled for development or comparison of dissimilar techniques and computational programs which are frequently used in this field of engineering.     

电力市场运营系统体系结构设计及实现技术

总结了电力市场运营系统的特点,如扩展性要求强、系统复杂、时序性和安全性高等。在对比分析客户端/服务器(C/S)与浏览器/服务器(B/S)体系结构和分布式组件技术的基础上,结合系统特点给出了C/S与B/S结构混合模式的一种实用可行的电力市场运营系统体系解决方案。讨论了基于IEC61970CIM电力系统国际标准模型的数据库平台、基于CORBA的分布式系统中间件平台、用XML进行数据交换以及采用电子商务运作模式等关键技术在电力市场运营系统中的应用。     

企业网站视频管理信息系统的设计与研究

随着互联网技术的高速发展,人们逐渐摆脱了图片加文字的平面媒体的信息传播方式,网络带宽提升更使以视频流媒体传输为基础的网络服务得到了广泛的应用。文章借鉴国内外大型视频网站开发经验,提出了企业网站视频管理信息系统的设计架构。详细阐述了视频转码、模板管理以及视频推送等功能模块。介绍了提高用户访问速度的相关技术,并对网站下一步建设提出了展望。     

基于版本化模型的配电网辅助设计系统的实现

分析了利用配网GIS系统进行工程辅助设计时存在的问题,并结合配电网规划应用需求,提出了一套完整的基于版本化模型技术的配电网辅助设计系统的开发技术和方法。基于版本化模型技术的配电网辅助设计系统实施之后,升级和改造了现有的GIS系统架构,实现了版本化编辑和存储,并在统一数据库对象、同图显示和编辑界面等方面实现了电网的统一建模。     

Towards human BCI applications based on cognitive brain systems: an investigation of neural signals recorded from the dorsolateral prefrontal cortex.

One of the critical issues in brain-computer interface (BCI) research is how to translate a person's intention into brain signals for controlling computer programs. The motor system is currently the primary focus, where signals are obtained during imagined motor responses. However, cognitive brain systems are also attractive candidates, in that they may be more amenable to conscious control, yielding better regulation of magnitude and duration of localized brain activity. We report on a proof of principle study for the potential use of a higher cognitive system for BCI, namely the working memory (WM) system. We show that mental calculation reliably activates the WM network as measured with functional magnetic resonance imaging (fMRI). Moreover, activity in the dorsolateral prefrontal cortex (DLPFC) indicates that this region is active for the duration of mental processing. This supports the notion that DLPFC can be activated, and remains active, at will. Further confirmation is obtained from a patient with an implanted electrode grid for diagnostic purposes, in that gamma power within DLPFC increases during mental calculation and remains elevated for the duration thereof. These results indicate that cortical regions involved in higher cognitive functions may serve as a readily self-controllable input for BCI applications. It also shows that fMRI is an effective tool for identifying function-specific foci in individual subjects for subsequent placement of cortical electrodes. The fact that electrocorticographic (ECoG) signal confirmed the functional localization of fMRI provides a strong argument for incorporating fMRI in BCI research.     

Utilizing gamma band to improve mental task based brain-computer interface design.

A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during mental tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different mental tasks. In this paper, the performance of the mental task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experimental study, EEG signals extracted during five mental tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two mental tasks. The results indicated that ((1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; (2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method.     

泛在电力物联网商业模式理论体系与设计架构

泛在电力物联网面向开放的市场和激烈的竞争,其商业模式亟须建立系统的方法论,为泛在电力物联网建设提供一套现实可行的商业逻辑参考。基于战略管理与商业模式相关理论,结合泛在电力物联网建设需求与特征,构建了相应的理论体系与设计架构。理论体系中明确了覆盖宏观、中观、微观的泛在电力物联网商业模式研究界面,并提出了对应的公司层、业务层和项目层商业模式研究与设计理论;设计架构主要围绕业务层与项目层商业模式设计,分析提炼六大要素,并形成了“三大环节、一个循环”的逻辑框架与“要素层-分析层-结果层-方案层”的设计原理,构建了e-IoT MAPPER商业模式引领者模型。