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

近年来，脑机接口(BCI)取得了长足的发展。到目前为止，它们的主要应用一直是针对身体残疾的人群，在这些人群中，它们通常是唯一的投入手段。在功能性近红外光谱(FNIRS)脑数据的实时测量和机器学习分类方面的最新结果导致了这个项目，PI和他的团队将开发和评估脑测量技术，将其作为输入到更大人群的适应性用户界面。在这种情况下，大脑输入被用作一种方式，可以毫不费力地直接从用户的大脑活动中获取有关用户及其上下文的更多信息，然后使用这些信息来实时调整用户界面。为了实现这一点，将在人和机器人之间引入多模式双重任务界面，这将作为评估这些新界面的有效性的特别敏感的试验台。该项目将在能够客观地测量将大脑输入引入界面对任务绩效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、多任务的情况下最有用。将采用该团队在人-机器人交互研究中经过仔细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。它将研究最近从低频(0.1赫兹)头皮不同区域之间的相位关系中获得的fNIRS信号，以及比先前检查的更大范围的传感器放置位置。随着这些通过机器学习和其他分析方法发展成可用于实时信号的测量，它们将被纳入新的用户界面。布罗德影响：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，这项技术将成为一个更强大的技术项目，其成果将有望为身体残疾的用户带来希望，并最终有望改善严重运动障碍者的生活。

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.