HCC: Medium: Bringing Brain-Computer Interfaces into Mainstream HCI

HCC：中：将脑机接口纳入主流 HCI

• 批准号: 1065154
• 负责人: Robert Jacob
• 金额: $ 93.55万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065154&HistoricalAwards=false
• 关键词: HCC; Medium; Bringing; Brain; Computer

Brain-computer interfaces (BCI) have made dramatic progress in recent years. Their main application to date has been for the physically disabled population, where they typically serve as the sole input means. Recent results on the real-time measurement and machine learning classification of functional near infrared spectroscopy (fNIRS) brain data lead to this project, in which the PI and his team will develop and evaluate brain measurement technology as input to adaptable user interfaces for the larger population. In this case, brain input is used as a way to obtain more information about the user and their context in an effortless and direct way from their brain activity, which is then used to adapt the user interface in real time. To accomplish this a multi-modal dual task interface between humans and robots will be introduced, which will serve as a particularly sensitive testbed for evaluating the efficacy of these new interfaces.The project will create and study these new user interfaces in domains where the effect on task performance of introducing the brain input to the interface can be measured objectively. They are most useful in demanding, high-performance, multitasking situations. Carefully calibrated multitasking applications scenarios from the team's research in Human-Robot Interaction will be employed. The project will also advance the range of fNIRS brain measurements that can be applied to user interfaces. It will study a recently identified fNIRS signal obtained from the phase relationships among different regions of the scalp at low frequencies (0.1 Hz), as well as a wider range of sensor placement locations than previously examined. As these are developed into usable measurements for real-time signals with machine learning and other analysis approaches, they will be incorporated into new user interfaces.Broader Impacts: The target of the research is adaptive interfaces for non-disabled users, where brain measurement is an additional source of user input. However, as the work proceeds toward making this into a more robust technology project outcomes will have promise for physically-challenged users, and ultimately they promise to improve the lives of people with severe motor disabilities.

脑机接口（BCI）近年来取得了巨大的进展。 到目前为止，它们的主要应用是针对身体残疾的人口，它们通常是唯一的输入手段。 最近对功能性近红外光谱（fNIRS）大脑数据的实时测量和机器学习分类的结果导致了这个项目，其中PI和他的团队将开发和评估大脑测量技术，作为更大人群的自适应用户界面的输入。 在这种情况下，大脑输入被用作以毫不费力和直接的方式从他们的大脑活动获得关于用户及其上下文的更多信息的方式，然后这些信息被用于真实的调整用户界面。 为了实现这一目标，将引入人类和机器人之间的多模态双任务界面，这将作为评估这些新界面功效的特别敏感的测试平台。该项目将在可以客观测量将大脑输入引入界面对任务性能的影响的领域中创建和研究这些新的用户界面。 它们在要求苛刻、高性能、多任务处理的情况下最有用。 将采用团队在人机交互研究中精心校准的多任务应用场景。该项目还将推进可应用于用户界面的fNIRS大脑测量范围。它将研究最近确定的fNIRS信号，该信号是从低频（0.1 Hz）头皮不同区域之间的相位关系中获得的，以及比以前检查的更广泛的传感器放置位置。 随着这些技术通过机器学习和其他分析方法发展成为实时信号的可用测量方法，它们将被纳入新的用户界面中。更广泛的影响：研究的目标是为非残疾用户提供自适应界面，其中大脑测量是用户输入的额外来源。 然而，随着这项工作的进展，使其成为一个更强大的技术项目的成果将有希望为身体有障碍的用户，并最终他们承诺改善生活的人与严重的运动障碍。