Geometric Pattern Analysis and Mental Task Design for a Brain-Computer Interface

脑机接口的几何图案分析和心理任务设计

• 批准号: 0208958
• 负责人: Charles Anderson
• 金额: $ 69.93万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0208958&HistoricalAwards=false
• 关键词: Geometric; Pattern; Analysis; Mental; Task

The goal of this project is to develop novel electroencephalogram (EEG) classification methods that result in a practical, real-time brain-computer interfaces (BCI) system. BCIs are hardware and software systems that sample EEG signals from electrodes placed on the scalp and extract patterns from EEG that indicate the mental activity being performed by the person. The long-term goal of this line of research is a new mode of communication for victims of diseases and injuries resulting in the loss of voluntary muscle control, such as amyotrophic lateral sclerosis (ALS), high-level spinal cord injuries or severe cerebral palsy. The autonomic and intellectual functions of such subjects continue to be active. This can result in a "locked-in" syndrome in which a person is unable to communicate to the outside world. The interpretation of information contained in EEG may lead to a new mode of communication with which subjects can communicate with their care givers or directly control devices such as televisions, wheel chairs, speech synthesizers and computers.The objectives of this project are the design and testing of an EEG system for experimentation in real-time EEG pattern analysis constructed of off-the-shelf components for under $5,000, development of new techniques for a novel approach to studying the cognitive components of mental tasks and how they vary in time and across subjects, demonstration that real-time feedback to the subject will produce a biofeedback situation in which the subject can learn to modify their EEG to increase classification accuracy, proof by demonstration that accuracy and classification time will be sufficient for two persons to interact over the net in a simple game controlled by two BCI systems. The evaluation of the results of this project in light of these objectives will be based on the accuracy of EEG classification, the speed with which the classification can be performed, and the expense of the EEG system and of its maintenance and extendibility.The most significant impact of this project to the disabled community will be an easier to use, affordable BCI system. The inclusion of a wide range of mental tasks will result in a better understanding of which mental tasks are easiest for subjects to consistently perform and for detection algorithms to reliably identify. Better BCI systems will also be significant for other classes of users who can benefit from augmented communication interfaces in applications that require extremely fast commands. The significance of this project to the BCI research community is the specification and testing of the inexpensive system for experimentation with EEG signal analysis. The system based on off-the-shelf components and software to be developed and made publicly available is expected to allow a number of additional research groups to enter the BCI field. Also, this project's results on the analysis of cognitive components in EEG measured during a wide range of mental tasks will broaden the set of mental activities available to users of BCI systems.

该项目的目标是开发新颖的脑电图 (EEG) 分类方法，从而形成实用的实时脑机接口 (BCI) 系统。 BCI 是一种硬件和软件系统，可对放置在头皮上的电极的脑电图信号进行采样，并从脑电图中提取表明人正在进行的心理活动的模式。 该研究的长期目标是为患有导致随意肌控制丧失的疾病和伤害的受害者提供一种新的沟通方式，例如肌萎缩侧索硬化症（ALS）、高水平脊髓损伤或严重脑瘫。 这些主体的自主和智力功能继续活跃。 这可能会导致“锁定”综合症，使人无法与外界沟通。 对脑电图所含信息的解释可能会带来一种新的通信模式，受试者可以通过该模式与其护理人员进行通信或直接控制电视、轮椅、语音合成器和计算机等设备。该项目的目标是设计和测试脑电图系统，用于实时脑电图模式分析实验，该系统由现成组件构成，价格不到 5,000 美元，开发用于研究认知认知的新方法的新技术。 心理任务的组成部分以及它们在时间和受试者之间的变化，证明对受试者的实时反馈将产生生物反馈情况，在这种情况下受试者可以学习修改他们的脑电图以提高分类准确性，通过证明准确性和分类时间足以让两个人在由两个 BCI 系统控制的简单游戏中通过网络进行交互。根据这些目标对该项目的结果进行评估将基于脑电图分类的准确性、执行分类的速度以及脑电图系统及其维护和可扩展性的费用。该项目对残疾人社区最显着的影响将是一个更易于使用、负担得起的脑机接口系统。 纳入广泛的心理任务将有助于更好地理解哪些心理任务最容易让受试者持续执行并让检测算法可靠地识别。 更好的 BCI 系统对于其他类别的用户也具有重要意义，他们可以从需要极快命令的应用程序中的增强通信接口中受益。 该项目对 BCI 研究界的意义在于对用于脑电图信号分析实验的廉价系统进行了规范和测试。 该系统基于即将开发和公开的现成组件和软件，预计将允许许多其他研究小组进入 BCI 领域。 此外，该项目对在各种心理任务期间测量的脑电图认知成分进行分析的结果将扩大 BCI 系统用户可用的心理活动范围。