Brain-computer interfaces (BCIs) employ various paradigms which afford intuitive, augmented control for users to navigate digital technologies. In this study we explore the application of these BCI concepts to predictive text systems: commonplace interactive and assistive tools with variable usage contexts and user behaviors. We conducted an experiment to analyze user neurophysiological responses under these different usage scenarios and evaluate the feasibility of a closed-loop, adaptive BCI for use with such technologies. We recorded electroencephalogram (EEG) and eye tracking (ET) data from participants while they completed a self-paced typing task in a simulated predictive text environment. Participants completed the task with different degrees of reliance on the predictive text system (completely dependent, completely independent, or their choice) and encountered both correct and incorrect text generations. Data suggest that erroneous text generations may evoke neurophysiological responses that can be measured with both EEG and pupillometry. Moreover, these responses appear to change according to users\u2019 reliance on the predictive text system. Results show promise for use in a passive, hybrid, BCI with a closed-loop, adaptive framework, and support a neurophysiological approach to the challenge of real-time human feedback on system performance.<\/p>\n","protected":false},"excerpt":{"rendered":"
Brain-computer interfaces (BCIs) employ various paradigms which afford intuitive, augmented control for users to navigate digital technologies. In this study we explore the application of these BCI concepts to predictive text systems: commonplace interactive and assistive tools with variable usage contexts and user behaviors. 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