Neurocognition-inspired design with machine learning

Pan Wang, Shuo Wang, Danlin Peng, Liuqing Chen, Chao Wu, Zhen Wei, Peter Childs, Yi-Ke GUO, Ling Li*

*Corresponding author for this work

    Research output: Contribution to journalJournal articlepeer-review

    7 Citations (Scopus)


    Generating designs 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 a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject's EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

    Original languageEnglish
    Article numbere33
    Number of pages19
    JournalDesign Science
    Publication statusPublished - 17 Dec 2020

    Scopus Subject Areas

    • Modelling and Simulation
    • Visual Arts and Performing Arts
    • Engineering(all)

    User-Defined Keywords

    • cognitive understanding
    • deep learning
    • generative adversarial networks
    • neurocognition-inspired design
    • neuromarketing
    • personalized design


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