Proceedings of the Annual Meeting of the Cognitive Science Society

Image classification aided by brain activity measured during rapid serial visual presentation (RSVP) shows promise to aidhuman viewers to quickly triage large volumes of images with support of an EEG technology. Fast perceptual responsesare parsed with a brain-activity classifier operating on EEG signals to select an image subset containing visual informationsimilar to the viewers target. However, current processes for training brain activity classifiers are experimentally andcomputationally expensive. We propose a deep learning model that classifies images based off of brain-activity. Usingthe satellite visual images and EEG data provided from Bigdely-Shamlo et al. (2007), we compare different machinelearning (Support Vector Machines) and deep learning (Convolutional Neural Networks and Recurrent Neural Networks)approaches along with different data manipulation styles for classifying the satellite images. This initial report summarizesthe efforts to establish benchmarks for deep learning, exploring the potential to streamline and improve brain-activity basedclassification.