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Neurocognition-inspired Design with Machine Learning

Wang, Pan, Wang, Shuo, Peng, Danlin, Wu, Chao, Wei, Zhen, Childs, Peter, Guo, Yike, Li, Ling (2020) Neurocognition-inspired Design with Machine Learning. Design Science, 6 (e33). E-ISSN 2053-4701. (doi:10.1017/dsj.2020.23) (KAR id:82586)


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.

Item Type: Article
DOI/Identification number: 10.1017/dsj.2020.23
Uncontrolled keywords: Cognition, Design, EEG, AI
Subjects: N Visual Arts > NK Decorative arts. Applied arts. Decoration and ornament
Q Science > Q Science (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Caroline Li
Date Deposited: 30 Nov 2020 01:01 UTC
Last Modified: 04 Mar 2024 17:38 UTC
Resource URI: (The current URI for this page, for reference purposes)

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