Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface.

Visual imagery, or the mental simulation of visual information from memory, could serve as an effective control paradigm for a brain-computer interface (BCI) due to its ability to directly convey the user's intention with many natural ways of envisioning an intended action. However, multiple initial investigations into using visual imagery as a BCI control strategies have been unable to fully evaluate the capabilities of true spontaneous visual mental imagery. One major limitation in these prior works is that the target image is typically displayed immediately preceding the imagery period.

EEG and Eye-Tracking Error-Related Responses During Predictive Text Interactions: A BCI Case Study.

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.