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

该项目的目标是开发新的脑电(EEG)分类方法，从而产生一种实用的实时脑机接口(BCI)系统。BCI是一种硬件和软件系统，它从放置在头皮上的电极上采样EEG信号，并从EEG中提取指示人正在进行的精神活动的模式。这一系列研究的长期目标是为肌萎缩侧索硬化症(ALS)、高水平脊髓损伤或严重脑瘫等疾病和损伤的受害者提供一种新的沟通模式。这些受试者的自主和智力功能继续活跃。这可能会导致一个人无法与外部世界交流的“闭锁”综合症。对包含在EEG中的信息的解释可能导致一种新的交流模式，受试者可以与他们的照顾者或直接控制诸如电视、轮椅、语音合成器和计算机的设备进行交流。该项目的目标是设计和测试EEG系统，用于实验实时EEG模式分析，该系统由不到5,000美元的现成组件构成，开发新技术用于研究心理任务的认知成分以及它们在时间上和在受试者之间如何变化的新方法，证明对受试者的实时反馈将产生一种生物反馈情况，其中受试者可以学习修改他们的EEG以提高分类精度，通过演示证明，准确率和分类时间将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。根据这些目标评估这个项目的结果将基于脑电分类的准确性、分类的速度、脑电系统及其维护和可扩充性的费用。这个项目对残疾社区最重要的影响将是一个更容易使用、负担得起的脑机接口系统。广泛的心理任务的纳入将导致更好地理解哪些心理任务最容易让受试者始终如一地完成，并使检测算法能够可靠地识别。更好的BCI系统对于其他类别的用户也将具有重要意义，这些用户可以在需要极快命令的应用程序中受益于增强的通信接口。该项目对脑-机接口研究界的重要意义在于指定和测试用于脑电信号分析实验的廉价系统。该系统以现成的组件和软件为基础，将开发并向公众提供，预计将允许更多的研究小组进入BCI领域。此外，该项目对脑电中认知成分的分析结果将扩大脑机接口系统用户可用的精神活动集。

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.