Translating Emotions from EEG to Visual Arts

Abstract

Exploring the potentialities of artificial intelligence (AI) in the world of arts is fundamental to understand and define how this technology is shaping our creativity. We propose a system that generates emotionally expressive paintings from EEG signals. The emotional information, encoded from the signals through a graph neural network, is inputted to a generative adversarial network (GAN), trained on a dataset of paintings. The design and experimental choices at the base of this work rely on the understanding that emotions are hard to define and formalize. Despite this, the proposed results witness an interaction between an AI system and a human, capable of producing an original and artistic re-interpretation of emotions. These results have a promising potential for AI technologies applied to visual arts.