Images from the Mind: BCI image evolution based on Rapid Serial Visual Presentation of polygon primitives
This provides a proof of concept for BCI-based image reconstruction, which could benefit applications in communication or assistive technology, though it is incremental as it builds on existing RSVP and BCI methods.
The paper tackles the problem of reconstructing visual images from a user's mind using EEG-based BCI technology, achieving an average classification accuracy of 75% for polygon selection and capturing over 65% of visual details on average, with 25% of images fully reconstructed.
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 build 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 $75\%$. $25\%$ 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.