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

Tufts University, Medford MA

Investigators

Abstract

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.