Brainput: enhancing interactive systems with streaming fnirs brain input

Authors:
Erin Solovey;Paul Schermerhorn;Matthias Scheutz;Angelo Sassaroli;Sergio Fantini;Robert Jacob
Affiliations:
Massachusetts Institute of Technology, Cambridge, MA, USA;Indiana University, Bloomington, Indiana, United States;Tufts University, Medford, Massachusetts, United States;Tufts University, Medford, Massachusetts, United States;Tufts University, Medford, Massachusetts, United States;Tufts University, Medford, Massachusetts, United States
Venue:
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Year:
2012

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Abstract

This paper describes the Brainput system, which learns to identify brain activity patterns occurring during multitasking. It provides a continuous, supplemental input stream to an interactive human-robot system, which uses this information to modify its behavior to better support multitasking. This paper demonstrates that we can use non-invasive methods to detect signals coming from the brain that users naturally and effortlessly generate while using a computer system. If used with care, this additional information can lead to systems that respond appropriately to changes in the user's state. Our experimental study shows that Brainput significantly improves several performance metrics, as well as the subjective NASA-Task Load Index scores in a dual-task human-robot activity.