Mind-Mirror: EEG-Guided Image Evolution

Abstract. We propose a brain-computer interface (BCI) system for evolving images in realtime based on subject feedback derived from electroencephalography (EEG). The goal of this system is to produce a picture best resembling a subject's `imagined' image. This system evolves images using Compositional Pattern Producing Networks (CPPNs) via the NeuroEvolution of Augmenting Topologies (NEAT) genetic algorithm. Fitness values for NEAT-based evolution are derived from a real-time EEG classifier as images are presented using rapid serial visual presentation (RSVP). Here, we report the design and performance, for a pilot training session, of a BCI system for real-time single-trial binary classification of viewed images based on participant-specific brain response signatures present in 128-channel EEG data. Selected training-session image clips created by the image evolution algorithm were presented in 2-s bursts at 8/s. The subject indicated by subsequent button press whether or no...

• Type: Journal
• Year: 2009
• Where: HCI
• Authors: Nima Bigdely Shamlo, Scott Makeig
• Keywords: Brain-computer Interface, HCI 2009, Human Computer Interaction, Image, Independent Component Analysis