Images from the Mind: BCI image evolution based on Rapid Serial Visual Presentation of polygon primitives

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 $75\%$. $25\%$ 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.