Verifying design through generative visualization of neural activity

Current neuroscience-focused approaches for evaluating the effectiveness of a design do not use direct visualization of mental activity. Inspired by S. Palazzo's team we proposed a framework with reconstruction of mental images when a design is presented. A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images. A generative adversarial network (GAN) conditioned on the EEG latent representation is trained for reconstructing these images. After training, the neural network is able to reconstruct images from brain activity recordings. To demonstrate the proposed method in the context of the mental association with a design, we performed a study that indicates an iconic design image could inspire the subject to create cognitive associations with branding and valued products. The proposed method could have potential in verifying designs by visualizing the cognitive understanding of underlying brain activity.