Images from the mind: BCI image evolution based on rapid serial visual presentation of polygon primitives

Abstract

This paper provides a proof of concept for an EEG-based reconstruction of a visual image which is on a user’s mind. Our approach is based on the rapid serial visual presentation (RSVP) of polygon primitives and brain-computer interface (BCI) technology. The presentation of polygons that contribute to building a target image (because they match the shape and/or color of the target) trigger attention-related EEG patterns. Accordingly, these target primitives can be determined using BCI classification of event-related potentials (ERPs). They are then accumulated in the display until a satisfactory reconstruction is reached. Selection steps have an average classification accuracy of 75%. Twenty-five percent of the images could be reconstructed completely, while more than 65% of the available visual details could be captured on average. Most of the misclassifications were not misinterpretations of the BCI concerning users’ intent; rather, users tried to select polygons that were different than what was intended by the experimenters. Open problems and alternatives to develop a practical BCI-based image reconstruction application are discussed.