Neurocognition-inspired Design with Machine Learning

Generating design via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). Firstly, we employ a recurrent neural network (LSTM - Long Short-Term Memory) as an encoder to extract EEG features from raw EEG signals; this data is recorded from subjects viewing several categories of images from ImageNet. Secondly, we train a GAN model conditioned on the encoded EEG features to generate design images. Thirdly, we use the model to generate design images from the subject’s EEG measured brain activity.