Images from the Mind: BCI image evolution based on Rapid Serial Visual Presentation of polygon primitives
Lu\'is F. Seoane, Stephan Gabler, Benjamin Blankertz

TL;DR
This study demonstrates a proof of concept for reconstructing images from users' minds using EEG signals and rapid presentation of polygon primitives, achieving 75% classification accuracy and partial image reconstruction.
Contribution
It introduces a novel BCI method combining RSVP of polygons with ERP classification for image reconstruction from EEG signals.
Findings
75% average classification accuracy
25% of images fully reconstructed
Over 65% of visual details captured
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 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 . of the images could be reconstructed completely, while more than of the available visual details could be captured on average. Most of the misclassifications were not misinterpretations of the…
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